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.
Citation to related publication:
Ozturk, Doga & Zou, Shasha & Slavin, James. (2016). The Response of the Coupled Magnetosphere-Ionosphere System to the 15 August 2015 Solar Wind Dynamic Pressure Enhancement. https://www.researchgate.net/publication/300020219
Raw data and analysis files for the figures corresponding to the manuscript submission entitled "CCL2 enhances macrophage inflammatory responses via miR-9 mediated downregulation of the ERK1/2 phosphatase Dusp6"
Alexander, Robert L., Sile O’Modhrain, D. Aaron Roberts, Jason A. Gilbert, and Thomas H. Zurbuchen. “The Bird’s Ear View of Space Physics: Audification as a Tool for the Spectral Analysis of Time Series Data.” Journal of Geophysical Research: Space Physics 119, no. 7 (2014): 5259–71. https://doi.org/10.1002/2014JA020025
The Evans Old Field Plant Database contains FileMaker and Excel files of data collected by Dr. Francis C. Evans during a 50-year study on successional change on Evans Old Field on the Edwin S. George Reserve. Data include plant phenology, location, and abundances observed from 1948 to 1997.
All animal-related procedures were approved by the University of Michigan Institutional Animal Care and Use Committee (Protocols #PRO00006234 and #PRO00008306) and the Peruvian government SERFOR (Servicio Nacional Forestal y de Fauna Silvestre. and Data were collected during five field expeditions in the Amazonian lowlands of Peru from March 2016 to December 2018.
This data set includes four zipped files each containing unprocessed cell images from a single cell line collected as raw data, the scripts used to process these images and tabular files with the processed data outputs. This data set supports the PLOS ONE publication, "Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning."
SWMF is used to study the segmentation of SED plume into polar cap patches during the geomagnetic storm on Sep 7, 2017. The database includes the 3D output in the upper atmosphere from GITM, the 2D output from Ionospheric Electrodynamics (IE) and 3D output from BATSRUS. The output from GITM can be read with thermo_batch_new.pro. The output from IE can be opened with Spacepy at https://pythonhosted.org/SpacePy/. The output from BATSRUS can be opened with tecplot.
More details can be found in Readme.txt.
Wang, Z., Zou, S., Coppeans, T., Ren, J., Ridley, A., & Gombosi, T. (2019). Segmentation of SED by Boundary Flows Associated With Westward Drifting Partial Ring current. Geophysical Research Letters, 46(14), 7920–7928. https://doi.org/10.1029/2019GL084041
This is data is a large assortment of over 50 1,4-polybutadiene star-linear blends that can be used for assessing and developing predictive models. The data are presented in CSV files.
Hall, R., Desai, P. S., Kang, B.-G., Huang, Q., Lee, S., Chang, T., Venerus, D. C., Mays, J., Ntetsikas, K., Polymeropoulos, G., Hadjichristidis, N., & Larson, R. G. (2019). Assessing the Range of Validity of Current Tube Models through Analysis of a Comprehensive Set of Star–Linear 1,4-Polybutadiene Polymer Blends. Macromolecules, 52(20), 7831–7846. https://doi.org/10.1021/acs.macromol.9b00642
The outer epithelial layer of zebrafish retinae contains a crystalline array of cone photoreceptors, called the cone mosaic. As this mosaic grows by mitotic addition of new photoreceptors at the rim of the hemispheric retina, topological defects, called “Y-Junctions”, form to maintain approximately constant cell spacing. The generation of topological defects due to growth on a curved surface is a distinct feature of the cone mosaic not seen in other well-studied biological patterns like the R8 photoreceptor array in the _ Drosophila compound eye. Since defects can provide insight into cell-cell interactions responsible for pattern formation, here we characterize the arrangement of cones in individual Y-Junction cores (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7) as well as the spatial distribution of Y-junctions across entire retinae (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae). We find that for individual Y-junctions, the distribution of cones near the core corresponds closely to structures observed in physical crystals (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7). In addition, Y-Junctions are organized into lines, called grain boundaries, from the retinal center to the periphery (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae and Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae). In physical crystals, regardless of the initial distribution of defects, defects can coalesce into grain boundaries via the mobility of individual particles. By imaging in live fish, we demonstrate that grain boundaries in the cone mosaic instead appear during initial mosaic formation, without requiring defect motion (see Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae and Dataset for analyzing Y-junction motion in live fish retinae). Motivated by this observation, we show that a computational model of repulsive cell-cell interactions generates a mosaic with grain boundaries (see Code and example simulations of phase-field crystal model (for cone mosaic formation)). In contrast to paradigmatic models of fate specification in mostly motionless cell packings (see Code and accompanying input data for simulating lateral inhibition on motionless cell packing), this finding emphasizes the role of cell motion, guided by cell-cell interactions during differentiation, in forming biological crystals. Such a route to the formation of regular patterns may be especially valuable in situations, like growth on a curved surface, where the resulting long-ranged, elastic, effective interactions between defects can help to group them into grain boundaries.