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- Creator:
- Luyet, Chloe, Elvati, Paolo, Vinh, Jordan, and Violi, Angela
- Description:
- A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low-THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell. We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.
- Keyword:
- molecular dynamics, membranes, mechanical vibration, bacterial identification, and Staphylococcus aureus
- Citation to related publication:
- Luyet C, Elvati P, Vinh J, Violi A. Low-THz Vibrations of Biological Membranes. Membranes. 2023; 13(2):139. https://doi.org/10.3390/membranes13020139
- Discipline:
- Engineering
- Creator:
- Rivera-Rivera, Luis Y., Moore, Timothy C., and Glotzer, Sharon C.
- Description:
- The dataset is organized as follows: the data for each of the three target structures is contained within a directory with the structure name (e.g., kagome, pyrocholore and snub-square). Within each structure directory, data obtained from alchemical and self-assembly simulations are separated into alchem and self-assembly directories respectively. An additional suboptimal-self-assembly directory is only present for the snub-square structure and contains the data for the pattern registration analysis discussed in the SI. For a detailed description of each file contained within each directory, please refer to the README file.
- Keyword:
- inverse design, self-assembly, triblock Janus particles, crystallization slot, and digital alchemy
- Citation to related publication:
- Rivera-Rivera, LY, Moore, TC & SC Glotzer. Inverse design of triblock Janus spheres for self-assembly of complex structures in the crystallization slot via digital alchemy. Soft Matter, 2023, 19, 2726-2736 doi: 10.1039/d2sm01593e
- Discipline:
- Engineering
- Creator:
- Wu, Ziyou and Revzen, Shai
- Description:
- The data in this repository is a nearly unique dataset at the time of its making -- precise measurements of all contact forces of a 6-legged robot during multi-legged slipping motions and regular walking. These data were collected to establish the validity of the observation presented in this article: Zhao et al. Walking is like slithering: A unifying, data-driven view of locomotion. (2022) PNAS 119(37): e113222119. DOI: https://doi.org/10.1073/pnas.2113222119
- Keyword:
- robot, locomotion, and multilegged
- Citation to related publication:
- Science Robotics paper being submitted
- Discipline:
- Engineering
- Creator:
- Gultekin, Kayhan, Nyland, Kristina, Gray, Nichole, Fehmer, Greg , Huang, Tianchi, Sparkman, Matthew, Reines, Amy E., Greene, Jenny E., Cackett, Edward M., and Baldassare, Vivienne
- Description:
- The data were processed using the scripted VLA calibration pipeline ( https://science.nrao.edu/facilities/vla/data-processing/pipeline/scripted-pipeline) for the Common Astronomy Software Applications (CASA) package version 5.3.0. Imaging was performed using the TCLEAN task in CASA with standard parameters appropriate for broadband data with point-source emissison. We used Briggs weighting with robust parameters between 0 and 1. Self calibration was implemented manually on an as-needed basis for sufficiently bright sources with evidence for residual phase errors in the image plane.
- Keyword:
- active galactic nuclei, radio galaxies, fundamental plane, and intermediate-mass black holes
- Citation to related publication:
- Gultekin et al. (2022) MNRAS, in press
- Discipline:
- Science
- Creator:
- Sugrue, Dennis P.
- Description:
- This data was collected and processed as part of ongoing research to characterize waterway infrastructure performance in the Great Lakes. These dataset enable researchers to evaluate both travel time and vessel carrying capacity in the waterway., I assembled AIS data from the MarineCadastre website for UTM Zones 15-18 for the years 2015-2017 available in csv format. I combined files for Navigation Seasons, defined as March to January and clipped data for a set of predefined features using a python code (AIS Data Processor.ipynb). The code writes the appended and clipped files to csv for a single Navigation Year. The written files are submitted here: Trimmed_NY2015_new.csv (n=13,228,824); Trimmed_NY2016_new.csv (n=18,782,779); Trimmed_NY2017_new.csv (n=16,816,603), Data fusion of AIS and LPMS used the following algorithm for a subset of 30 vessels on the waterway. Let A be the original AIS data and let B be the subset of records for vessel i within geographic feature j. The script for this analysis is attached (Maritime Data Fusion.ipynb), For Connecting Channels and select segments of the Great Lakes: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic feature, Gj. Let B_ij⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as vessel i arrival to feature j, b_ijt | 4. IF feature j is a harbor or lock, select tmax for each unique date or any consecutive dates, record as departure from feature j, b_ijt | 5. Calculate time elapsed between features for each vessel, For vessel passage through the Soo Locks: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic boundaries (46.5<Lat<46.6, -84.4<Lon<-84.3). Let C_(i,lock)⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as arrival to Soo Locks | 4. Select tmax for each unique date or any consecutive dates, record as departure to Soo Locks | 5. Calculate time delta between arrival and departure times, and The merged dataset is included here along with the raw LPMS data: Merged_Data_new.csv (n=42,021), LPMS obscured.csv (n=55,342). VesselNames have been obscured in these datasets to protect proprietary information for shipping companies.
- Keyword:
- Maritime Transportation Efficiency, Data Fusion, Waterway Performance
- Citation to related publication:
- Sugrue, D., Adriaens, P. (in review) Multi-dimensional Data Fusion to Evaluate Waterway Performance: Maritime Transport Efficiency of Iron Ore on the Great Lakes. Water Resources Research.
- Discipline:
- Engineering
- Creator:
- Hegedus, Alexander M
- Description:
- This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891 , The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information. , This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements: , and CASA 4.7.1 (or greater?) built on python 2.7 Example link for Red Hat 7 https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz Users may follow this guide to download and install the correct version of CASA for their system https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing CASA executables should be fairly straightforward to extract from the untarred files. gcc 4.8.5 or above (or below?) GCC installation instructions can be found here: https://gcc.gnu.org/install/ SPICE (I use cspice here) https://naif.jpl.nasa.gov/naif/toolkit_C.html As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc' '/home/alexhege/SPICE/LunarEph/moon_080317.tf' '/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf' '/home/alexhege/SPICE/LunarEph/pck00010.tpc' '/home/alexhege/SPICE/LunarEph/naif0008.tls' '/home/alexhege/SPICE/LunarEph/de430.bsp' ) All of which can be found at https://naif.jpl.nasa.gov/pub/naif/generic_kernels/ SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
- Citation to related publication:
- Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
- Discipline:
- Engineering and Science