Transcriptional accessibility of chromatin is central to guiding CD4+ T cell function through regulation of lineage specific gene expression. Myst1 is a histone acetyltransferase responsible for acetylation of the protein tail of histone 4 at lysine residue 16 (H416ac), resulting in increased transcriptional accessibility and activation of gene transcription. Previous studies have described a role for Myst1 in governing lymphocyte development in the thymus, however the role of Myst1 and H4K16ac in guiding activation of peripheral CD4+ T cells has not been studied. Activation of human and murine CD4+ T cells resulted in upregulation of Myst1 expression, and deletion of Myst1 resulted in changes in proliferative responses to both polyclonal stimulus and exogenous cytokines. Myst1-deficient T cells also exhibited modulations in lineage commitment, with decreased function in TH1/TH2 skewing conditions and increased function in response to TH17-promoting conditions. Regulation of Myst1 function in CD4+ T cells appears governed at least in part by STAT5, as Myst1 expression is regulated by STAT5 expression and DNA binding, and modulations in H4K16ac in Myst1-deficient CD4+ T cells is observable at sites in the promoter regions of lineage specific genes following skewing to the TH1 or TH2 lineage in vitro. Taken together, these results indicate an important role for the STAT5-Myst1 epigenetic axis in governing the activation and effector function of CD4+ T cells.
Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.
The rapid increases in solar wind dynamic pressure, termed sudden impulses (SIs), compress Earth’s dayside magnetosphere and strongly perturb the coupled Magnetosphere-Ionosphere (M-I) system. The compression of the dayside magnetosphere launches magnetohydrodynamic (MHD) waves, which propagate down to the ionosphere, changing the Auroral Field Aligned Currents (FACs), and into nightside magnetosphere. The global response to the compression front sweeping through the coupled system is not yet fully understood due to the sparseness of the measurements, especially those with the necessary time resolution to resolve the propagating disturbances. That’s why a study including modeling is necessary. On 15 August 2015 at 7.44 UT, Advanced Composition Explorer measured a sudden increase in the solar wind dynamic pressure from 1.11 nPa to 2.55 nPa as shown in Figure-1.
We use the magnetospheric spacecraft in the equatorial magnetosphere to identify the signatures of magnetosphere response to this SI event and examine the interaction of the propagating disturbances with the M-I system. With the increased time resolution of Active Magnetosphere and Polar Electrodynamics Response Experiment (AMPERE), the FAC pattern and intensity change due to SI can also be studied in more depth. We further use measurements from ground based magnetometer stations to increase our tracking capability for the disturbances in the ionosphere and to improve our understanding of their propagation characteristics. This is the first step in a comprehensive multi-point observation and a global magnetohydrodynamic simulation based investigation of the response of the coupled M-I system to sudden impulses.
Mathematica Diffusion Simulation: Programmed by Coburn, Caleb. Simulation of diffusion in organic heterostructures, including least square fits and statistical goodness of fit analysis. Used to calculate fits to transient data in Fig 1, 3 and Extended Data Fig.2. Example data file included for download
Matlab Montecarlo simulation: Programmed by Coburn, Caleb. Montecarlo simulation of charge diffusion on a cubic lattice to determine lateral diffusion length as a function of barrier height, assuming thermionic emission over the barrier.
Matlab 2D Diffusion Simulation:Programmed by Coburn, Caleb. Modified from BYU Physics 430 Course Manual. Simulates diffusion around a film discontinuity, such a cut. Used to generate fits to Extended Data Fig. 1
Burlingame, Q., Coburn, C., Che, X., Panda, A., Qu, Y., & Forrest, S. R. (2018). Centimetre-scale electron diffusion in photoactive organic heterostructures. Nature, 554(7690), 77-80. https://doi.org/10.1038/nature25148
Li, Y., M. C. Barth, G. Chen, E. G. Patton, S.-W. Kim, A. Wisthaler, T. Mikoviny, A. Fried, R. Clark, and A. L. Steiner (2016), Large-eddy simulation of biogenic VOC chemistry during the DISCOVER-AQ 2011 campaign, J. Geophys. Res. Atmos., 121, 8083–8105. https://doi.org/10.1002/2016JD024942
Included are RegCM simulations driven by three different types of boundary conditions 1. ERA - present day only (1979-2005) 2. GFDL - present day (1978-2005) and future (2041-2065) 3. HadGEM - present day (1978-2005) and future (2041-2065) Each directory has three files with monthly averaged values: ATM: includes 4D (t,z,y,x) atmospheric fields (pressure, winds, temperature, specific humidity, cloud water) and some 3D fields (t,y,x) precipitation, soil temperature, soil water SRF: includes 3D (t,y,x) surface variables (surface pressure, 10m winds, drag coefficient, surface temperature, 2m air temperature, soil moisture, precipitation, runoff, snow, sensible heat flux, latent heat flux, surface radiation components (SW, LW), PBL height, albedo, sunshine duration) RAD: includes 4D radiative transfer variables (SW and LW heating, TOA fluxes, cloud fraction, ice water content) clm_h0 files: CLM land surface files, includes canopy variables, surface fluxes, soil moisture by layers, etc. "
Bryan, A. M., A. L. Steiner, and D. J. Posselt (2015), Regional modeling of surface-atmosphere interactions and their impact on Great Lakes hydroclimate, J. Geophys. Res. Atmos., 120, 1044–1064. https://doi.org/10.1002/2014JD022316