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  • Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

    work
    Creator:
    Saldinger, Jacob, Raymond, Matt , Elvati, Paolo, and Violi, Angela
    Description:
    The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at  https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at  https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at  https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at  https://software.rcc.uchicago.edu/mscg/
    Keyword:
    Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
    Citation to related publication:
     https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
    Discipline:
    Science

  • VISTA TEC database

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    Creator:
    Sun, Hu, Ren, Jiaen, Chen, Yang, and Zou, Shasha
    Description:
    Our research focuses on providing a fully-imputed map of the worldwide total electron content with high resolution and spatial-temporal smoothness. We fill in the missing values of the original Madrigal TEC maps via estimating the latent feature of each latitude and local time along the 2-D grid and give initial guess of the missing regions based on pre-computed spherical harmonics map. The resulting TEC map has high imputation accuracy and the ease of reproducing. and All data are in HDF5 format and are easy to read using the h5py package in Python. The TEC map is grouped in folders based on years and each file contains a single-day data of 5-min cadence. Each individual TEC map is of size 181*361.
    Keyword:
    Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
    Citation to related publication:
    Sun, H., Hua, Z., Ren, J., Zou, S., Sun, Y., & Chen, Y. (2020). Matrix Completion Methods for the Total Electron Content Video Reconstruction. arXiv preprint arXiv:2012.01618. and Zou, S., Ren, J., Wang, Z., Sun, H., & Chen, Y. (2021). Impact of Storm-Enhanced Density (SED) on Ion Upflow Fluxes During Geomagnetic Storm. Frontiers in Astronomy and Space Sciences, 162.
    Discipline:
    Science

  • Data for Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve

    work
    Creator:
    Liemohn, Michael W, Adam, Joshua G, and Ganushkina, Natalia Y
    Description:
    Many statistical tools have been developed to aid in the assessment of a numerical model’s quality at reproducing observations. Some of these techniques focus on the identification of events within the data set, times when the observed value is beyond some threshold value that defines it as a value of keen interest. An example of this is whether it will rain, in which events are defined as any precipitation above some defined amount. A method called the sliding threshold of observation for numeric evaluation (STONE) curve sweeps the event definition threshold of both the model output and the observations, resulting in the identification of threshold intervals for which the model does well at sorting the observations into events and nonevents. An excellent data-model comparison will have a smooth STONE curve, but the STONE curve can have wiggles and ripples in it. These features reveal clusters when the model systematically overestimates or underestimates the observations. This study establishes the connection between features in the STONE curve and attributes of the data-model relationship. The method is applied to a space weather example.
    Keyword:
    space physics, statistical methods, and STONE curve
    Citation to related publication:
    Liemohn, M. W., Adam, J. G., & Ganushkina, N. Y. (2022). Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve. Space Weather, 20, e2022SW003102.  https://doi.org/10.1029/2022SW003102
    Discipline:
    Science

  • Stormtime energetics: Energy transport across the magnetopause in a global MHD simulation, simulation and analysis data

    work
    Creator:
    Brenner, Austin, M
    Description:
    Coupling between the solar wind and magnetosphere can be expressed in terms of energy transfer through the separating boundary known as the magnetopause. Geospace simulation is performed using the Space Weather Modeling Framework (SWMF) of a multi-ICME impact event on February 18-20, 2014 in order to study the energy transfer through the magnetopause during storm conditions. The magnetopause boundary is identified using a modified plasma $\beta$ and fully closed field line criteria to a downstream distance of $-20R_{e}$. Observations from Geotail, Themis, and Cluster are used as well as the Shue 1998 model to verify the simulation field data results and magnetopause boundary location. Once the boundary is identified, energy transfer is calculated in terms of total energy flux \textbf{K}, Poynting flux \textbf{S}, and hydrodynamic flux \textbf{H}. Surface motion effects are considered and the regional distribution of energy transfer on the magnetopause surface is explored in terms of dayside $\left(X>0\right)$, flank $\left(X<0\right)$, and tail cross section $\left(X=X_{min}\right)$ regions. It is found that total integrated energy flux over the boundary is nearly balanced between injection and escape, and flank contributions dominate the Poynting flux injection. Poynting flux dominates net energy input, while hydrodynamic flux dominates energy output. Surface fluctuations contribute significantly to net energy transfer and comparison with the Shue model reveals varying levels of cylindrical asymmetry in the magnetopause flank throughout the event. Finally existing energy coupling proxies such as the Akasofu $\epsilon$ parameter and Newell coupling function are compared with the energy transfer results.
    Keyword:
    Space plasma, Magnetosphere, MHD simulations, Magnetopause, Substorm, Energy transfer, and Poynting flux
    Citation to related publication:
    Brenner A, Pulkkinen TI, Al Shidi Q and Toth G (2021) Stormtime Energetics: Energy Transport Across the Magnetopause in a Global MHD Simulation. Front. Astron. Space Sci. 8:756732. doi: 10.3389/fspas.2021.756732
    Discipline:
    Science

  • Connecting Concepts in the Brain by Mapping Cortical Representations of Semantic Relations

    work
    Creator:
    Zhang, Yizhen
    Description:
    We collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words to understand how the brain stores and connects different concepts. and This is a dataset for the paper: Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13.  https://doi.org/10.1038/s41467-020-15804-w. This project is also documented at  https://osf.io/eq2ba/.
    Keyword:
    fMRI, natural story comprehension, neural encoding, semantic processing, word relations, and naturalistic stimuli
    Citation to related publication:
    Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13.  https://doi.org/10.1038/s41467-020-15804-w
    Discipline:
    Science

  • Rotary spectra of surface kinetic energy in drifters and high-resolution global ocean models

    work
    Creator:
    Brasch, Jonathan M, Elipot, Shane, and Arbic, Brian
    Description:
    For Drifters, HYCOM, MITgcm: Spectra and kinetic energy files. Please see readme.txt for a description of all data and code contained here. and - Compare kinetic energies (KE) of high-resolution global ocean models estimated from rotary spectra to KE in surface drifter observations. - Near-inertial KE is closer to drifter observations in models with frequently updated wind forcing - Internal tide KE is closer to drifter observations in models with topographic wave drag
    Keyword:
    oceanography, rotary spectra, kinetic energy, sea surface velocity, and drifters
    Citation to related publication:
    Elipot, S., Lumpkin, R., Perez, R. C., Lilly, J. M., Early, J. J., & Sykulski, A. M. (2016). A global surface drifter data set at hourly resolution. Journal of Geophysical Research: Oceans, 121(5), 2937–2966.  https://doi.org/10.1002/2016JC011716
    Discipline:
    Science

  • Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

    work
    Creator:
    Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
    Description:
    We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
    Keyword:
    neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
    Citation to related publication:
    Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7.  https://doi.org/10.3389/fspas.2020.00042
    Discipline:
    Science and Engineering

  • Authenticated Boot Acceleration Algorithm

    work
    Creator:
    Nasser, Ahmad and Gumise, Wonder
    Description:
    The work on accelerating authenticated boot for embedded system resulted in designing an algorithm in python to perform the random address generation and cryptographic MAC calculation. The Sampled Boot schemes implemented in this package allow a significant reduction of the time needed to authenticate firmware images during startup, while still retaining a high degree of trust. This is particularly useful for automotive applications in which startup time constraints make secure boot a time prohibitive process. and Citation for this dataset: Nasser, A., Gumise, W. (2019). Authenticated Boot Acceleration Algorithm [Code and data]. University of Michigan Deep Blue Data Repository.  https://doi.org/10.7302/yeh1-1x17
    Keyword:
    Trusted Computing, IOT security, Embedded Security, and Cyber Physical Systems
    Citation to related publication:
    Nasser, A., Gumise, W., and Ma, D., "Accelerated Secure Boot for Real-Time Embedded Safety Systems," SAE Int. J. Transp. Cyber. & Privacy 2(1) : 35-48, 2019,  https://doi.org/10.4271/11-02-01-0003
    Discipline:
    Science

  • Data Pertaining to Initial Simulations Using the Conductance Model for Extreme Events

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    Creator:
    Mukhopadhyay, Agnit, Daniel T Welling, Michael W Liemohn, Aaron J Ridley, Shibaji Chakrabarty, and Brian J Anderson
    Description:
    An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events., Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events., and Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly.
    Keyword:
    Space Weather Forecasting, Extreme Weather, Ionosphere, Magnetosphere, MI Coupling, Ionospheric Conductance, Auroral Conductance, Aurora, SWMF, SWPC, Nonlinear Regression, and dB/dt
    Citation to related publication:
    Mukhopadhyay, A., Welling, D. T., Liemohn, M. W., Ridley, A. J., Chakraborty, S., & Anderson, B. J. (2020). Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts. Space Weather, 18(11), e2020SW002551.  https://doi.org/10.1029/2020SW002551
    Discipline:
    Engineering and Science

  • Dataset for Modeling Reconnection-driven Dynamics at Ganymede's Upstream Magnetosphere

    work
    Creator:
    Zhou, Hongyang
    Description:
    The largest moon in the solar system, Ganymede, is the only moon known to possess a strong intrinsic magnetic field and a corresponding magnetosphere. Using the latest version of Space Weather Modeling Framework (SWMF), we study the upstream plasma interactions and dynamics in this sub-Alfvenic system. Results from the Hall MHD and the coupled MHD with embedded Particle-in-Cell (MHD-EPIC) models are compared. We find that under steady upstream conditions, magnetopause reconnection occurs in a non-steady manner. Flux ropes of Ganymede's radius in length form on the magnetopause at a rate about 2/minute and create spatiotemporal variations in plasma and field properties. Upon reaching proper grid resolutions, the MHD-EPIC model can resolve both electron and ion kinetics at the magnetopause and show localized non-gyrotropic behavior inside the diffusion region. The estimated global reconnection rate from the models is about 80 kV with 60% efficiency, and there is weak evidence of about 1 minute periodicity in the temporal variations due to the dynamic reconnection process.
    Keyword:
    MHD, PIC, Ganymede, and magnetosphere
    Citation to related publication:
    Zhou, H., Tóth, G., Jia, X., & Chen, Y. (2020). Reconnection-Driven Dynamics at Ganymede’s Upstream Magnetosphere: 3-D Global Hall MHD and MHD-EPIC Simulations. Journal of Geophysical Research: Space Physics, 125(8), e2020JA028162.  https://doi.org/10.1029/2020JA028162
    Discipline:
    Science