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.
Introduction: Diagnostic testing is common in the emergency department. The value of some testing is questionable. The purpose of this study was to assess how varying levels of benefit, risk, and costs influenced an individual’s desire to have diagnostic testing.
Methods: A survey through Amazon Mechanical Turk presented hypothetical clinical situations: low risk chest pain and minor traumatic brain injury. Each scenario included three given variables (benefit, risk, and cost), that was independently randomly varied over four possible values (0.1%, 1%, 5%, 10% for benefit and risk and $0, $100, $500, and $1000 for the individual’s personal cost for receiving the test). Benefit was defined as the probability of finding the target disease (traumatic intracranial hemorrhage or acute coronary syndrome).
Results: A total of 1000 unique respondents completed the survey. Increasing benefit from 0.1% to 10%, the percent of respondents who accepted a diagnostic test went from 28.4% to 53.1%. [OR: 3.42 (2.57-4.54)] As risk increased from 0.1% to 10%, this number decreased from 52.5% to 28.5%. [OR: 0.33 (0.25-0.44)] Increasing cost from $0 to $1000 had the greatest change of those accepting the test from 61.1% to 21.4%, respectively. [OR: 0.15 (0.11-0.2)]
Conclusions: The desire for testing was strongly sensitive to the benefits, risks and costs. Many participants wanted a test when there was no added cost, regardless of benefit or risk levels, but far fewer elected to receive the test as cost increased incrementally. This suggests that out of pocket costs may deter patients from undergoing diagnostic testing with low potential benefit.
This random sample of OA articles comes from Deep Blue <deepblue.lib.umich.edu/documents>, the University of Michigan’s institutional repository service. Each OA article has the following characteristics: Prior to a known date (ranging from 2006 to the 2013) these articles—the final published version—were only available by subscription. After that date, they became freely available via Deep Blue. Meanwhile, other articles from the same journal issue as the now-OA article continued to only be available to subscribers. None of the OA articles were self-selected; authors did not choose to deposit the articles in question in Deep Blue, since we made them open via blanket licensing agreements between the publishers and the library.
Ottaviani J (2016) The Post-Embargo Open Access Citation Advantage: It Exists (Probably), It’s Modest (Usually), and the Rich Get Richer (of Course). PLoS ONE 11(8): e0159614. https://doi.org/10.1371/journal.pone.0159614
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.
