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- Creator:
- Burgin, Tucker and Mayes, Heather B.
- Description:
- This project aimed to discover and analyze the molecular mechanism of synthesis of two particular fucosylated oligosaccharide products in a mutant enzyme, Thermatoga maratima Alpha-L-Fucosidase D224G, whose wild type performs the opposite reaction (cleavage of fucosyl glycosidic bonds). Discovery of the mechanism was performed using an unbiased simulations method known as aimless shooting, whereas analysis of the mechanism in terms of the energy profile was performed using a separate method known as equilibrium path sampling. The data here concerns the latter method. and The contents of the atesa_master.zip are the ATESA GitHub project. A Python program for automating transition path sampling with aimless shooting using Amber. https://github.com/team-mayes/atesa
- Keyword:
- Equilibrium Path Sampling, Transition Path Sampling, Enzymatic Mechanism, and GH29
- Citation to related publication:
- Burgin, T., & Mayes, H. B. (2019). Mechanism of oligosaccharide synthesis via a mutant GH29 fucosidase. Reaction Chemistry & Engineering, 4(2), 402–409. https://doi.org/10.1039/C8RE00240A
- Discipline:
- Engineering
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- Creator:
- Ruas, Terry, Ferreira, Charles H. P., Grosky, William, França, Fabrício O., and Medeiros, Débora M. R,
- Description:
- The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems. Github: https://github.com/truas/LexicalChain_Builder
- Keyword:
- document classification, lexical chains, word embeddings, synset embeddings, chain2vec, and natural language processing
- Citation to related publication:
- Terry Ruas, Charles Henrique Porto Ferreira, William Grosky, Fabrício Olivetti de França, Débora Maria Rossi de Medeiros, "Enhanced word embeddings using multi-semantic representation through lexical chains", Information Sciences, 2020, https://doi.org/10.1016/j.ins.2020.04.048
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Whittaker, Collin B.
- Description:
- This study follows after work conducted first for my dissertation and is presently being prepared for journal submission. The goal of our analysis was to analyze a small design space for an electrospray array thruster---varying the geometry of its emitters, the size of its extractor apertures, and its operating voltage---to determine designs robust to uncertainty. That is, we use a model for array performance whose input parameters we treat as uncertain (stemming from approximations to higher-order physics, manufacturing tolerances in fabricating a thruster, and so on). Making these predictions as a function of design, then, we can identify configurations that are performant robust to this uncertainty (i.e., still meet required performance specifications with high confidence). The data which inform this trade study are taken pricipally from our pending manuscript "Emitter Model Inference from Electrospray Array Thruster Tests", and from my thesis, "Designing Porous Electrospray Array Thrusters Under Uncertainty" (linked to the dataset as published). The analysis was conducted in January and February of 2025. This work was supported by a NASA Space Technology Graduate Research Opportunity (80NSSC21K1247). This research was also supported in part through computational resources and services provided by Advanced Research Computing, a division of Information and Technology Services at the University of Michigan, Ann Arbor.
- Keyword:
- Electrospray, Electric propulsion, Robust optimization, Bayesian inference, and Ionic liquid ion source
- Discipline:
- Engineering
-
- Creator:
- Whittaker, Collin B
- Description:
- The object of our study was to train a reduced-fidelity model for individual emitter behavior within a porous conical type electrospray array thruster on data taken over the entire array, which is the sum over all the emitters. By leveraging surface profilometry to measure the variance in geometry in the array, we then gain insight into the individual emitter dynamics. By rigorously predicating uncertainty in the predictions made by the model on uncertainty over its inputs, we can then understand the major sources of uncertainty in the system. The raw experimental data which inform this inference and prediction study were acquired in April of 2024 at the Jet Propulsion Laboratory's MicroPropulsion Laboratory, with special thanks to Colleen Marrese-Reading and Steven Arestie. These and other results are reported in a separate manuscript: C. B. Whittaker, B. A. Jorns, S. M. Arestie, and C. M. Marrese-Reading, in 38th International Electric Propulsion Conference (Electric Rocket Propulsion Society, 2024) p. 730. The thruster used in these experiments was fabricated at the University of Michigan in March of 2024. The analysis underlying this work was conducted from September of 2024 to January of 2025. This work was supported by a NASA Space Technology Graduate Research Opportunity (80NSSC21K1247). This research was also supported in part through computational resources and services provided by Advanced Research Computing, a division of Information and Technology Services at the University of Michigan, Ann Arbor. Finally, this work was performed in part at the University of Michigan Lurie Nanofabrication Facility.
- Keyword:
- Electrospray, Electric propulsion, Ionic liquid ion source, Bayesian inference, and Profilometry
- Discipline:
- Engineering
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- Creator:
- Chen, Hongfan, Chen, Yang, Huang, Zhenguang, Zou, Shasha, Huan, Xun, and Toth, Gabor
- Description:
- Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH), which can be used to calculate the Geomagnetically Induced Currents (GICs), is crucial for estimating the space weather impact of geomagnetic disturbances. In this work, we develop a new data-driven model GeoDGP using deep Gaussian process (DGP), which is a Bayesian non-parametric approach. The model provides global probabilistic forecasts of dBH at 1-minute time cadence and with arbitrary spatial resolutions. We evaluate the model comprehensively on a wide range of geomagnetic storms, including the 2024 Gannon extreme storm. The results show that GeoDGP significantly outperforms the state-of-the-art physics-based first-principles Space Weather Modeling Framework (SWMF) Michigan Geospace model and the data-driven DAGGER model.
- Keyword:
- Space Weather, Uncertainty Quantification, Machine Learning, and Bayesian Inference
- Citation to related publication:
- Chen, H., et al. (2024). GeoDGP: One-Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting using Deep Gaussian Process.
- Discipline:
- Science and Engineering
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- Creator:
- Chen, Hongfan, Sachdeva, Nishtha, Huang, Zhenguang, van der Holst, Bart, Manchester, Ward, Jivani, Aniket, Zou, Shasha, Chen, Yang, Huan, Xun, and Toth, Gabor
- Description:
- In this study, we show that coronal mass ejection (CME) simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) coronagraph observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. L1 observations are used to constrain the background solar wind, while LASCO coronagraph observations filter the initial ensemble simulations by constraining the simulated CME propagation speed. We then construct probabilistic predictions for CME arrival time using the data-assimilated ensemble. Scripts in this work are written in R, Python and Julia.
- Keyword:
- Data Assimilation, Uncertainty Quantification, and Space Weather
- Citation to related publication:
- https://doi.org/10.1029/2024SW004165
- Discipline:
- Engineering
-
- Creator:
- Steiner, Allison L., Wozniak, Matthew, Kort, Eric, and DeCola, Phil
- Description:
- Airborne pollen can impact human health by causing seasonal allergies and contribute to the total amount of particulate matter in the atmosphere. Current observations of pollen are limited in both space and time, making it is difficult to accurately forecast how pollen is released into the environment. Lidar is a ground-based remote sensing technique that can identify particles in the atmosphere, and depolarized light can identify irregularly shaped particles like pollen. We deployed a ground-based lidar with depolarization at a forested site in northern Michigan during the spring tree pollination season to understand the timing and contribution of pollen to the total amount of particulate matter in the atmosphere. We identify nine pollen events at the forested site that lead to high particulate matter in the atmosphere. This dataset includes the processed lidar data using the MiniMPL raw event count , which is calibrated and normalized to calculate the normalized relative backscatter (NRB) as a function of height (Ware et al., 2016).
- Keyword:
- lidar; University of Michigan Biological Station; aerosols; depolarization
- Citation to related publication:
- Steiner, A.L., et al. Lidar-based observations of pollen above a mixed hardwood forest in the United States. Submitted.
- Discipline:
- Engineering
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- Creator:
- Aksoy, Doruk and Kim, Donghak
- Description:
- This dataset contains snapshots from simulations of a hexagonal self oscillating gel sheet defined via a triangular lattice. The lattice has stretching springs between neighboring vertices and bending springs with energy proportional to the square of the angle between neighboring traingular faces. The motion of the lattice is driven by time- and space-varying distributions of the rest lengths of the stretching springs. In the motivating experiments on thin gel sheets, there are chemical waves, radial or spiral in form, that induce local swelling of the sheets. As a simple model, this dataset considers radial or planar (unidirectional) traveling waves in the simulations. The sheet is modeled as a flat hexagon of radius 1 with an equilateral triangular triangle lattice mesh, with initially uniform mesh spacing of 1/33, resulting in 3367 mesh points. A small out-of-plane perturbation is applied and the motion evolves over the sheet over time. The sheet is modeled to have damped dynamics. However for large enough wave amplitudes, the sheet rapidly buckles into shapes with time-varying distributions of curvature, large in magnitude. For more information on the simulation that generated the data, please refer to "Semi-implicit methods for the dynamics of elastic sheets,” at Journal of Computational Physics by Alben et al. For an example SciML application that considers this dataset, please refer to "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications by Aksoy et al.
- Keyword:
- Soft robotics, Partial Differential Equations, Scientific Simulations, and Chaotic Systems
- Citation to related publication:
- Alben, Silas, et al. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics 399 (2019): 108952., Aksoy, Doruk, et al. "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications 34.9 (2022): 6879-6905., Aksoy, Doruk, et al. "An incremental tensor train decomposition algorithm." SIAM Journal on Scientific Computing 46.2 (2024): A1047-A1075., and Aksoy, Doruk, and Alex A. Gorodetsky. "Incremental Hierarchical Tucker Decomposition." arXiv preprint arXiv:2412.16544 (2024).
- Discipline:
- Engineering and Science
-
- Creator:
- Zhu, Yongxian, Deng, Sidi, and Cooper, Daniel R
- Description:
- This dataset is curated as a byproduct of the "Material and Vehicle Design for High-Value Recycling of Aluminum and Steel Automotive Sheet" project, funded by the REMADE Institute of the Department of Energy and referred to as the "Clean Sheet Project" in the file "electricity scenarios slides.pptx." The dataset presents projected U.S. electricity emission factors (MJ primary energy or gCO2/kWh electricity delivered) under various scenarios, including different levels of uptake of the U.S. Inflation Reduction Act. The projections are based on estimated trends in the U.S. electricity generation mix, along with the authors' analysis of the energy and emission intensities of relevant power sources. The dataset supports research—particularly life cycle assessment—relying on U.S. regional energy profile and emissions factors.
- Keyword:
- Electricity Mix, Renewable Energy, Greenhouse Gas Emissions, Decarbonization, and Net-Zero
- Discipline:
- Engineering and General Information Sources
-
- Creator:
- Crumley, Kelly M, Bealer, Elizabeth J, Lietzke, Anne C, Soleimanpour, Scott A, and Shea, Lonnie D
- Description:
- The research described here includes a combination of in vivo animal studies and in vitro cell studies. The animal studies were conducted in NSG mice purchased from Jackson Laboratories and involve implantation of a cell-laden scaffold, animal monitoring, and scaffold explantation. After explantation, scaffolds could be analyzed using PCR or staining. The in vitro cell studies involved administration of Exendin-4 during differentiation of hPSC-derived beta cells and had endpoints such as PCR, flow cytometry, and staining.
- Citation to related publication:
- Crumbley, Bealer, Lietzke, Soleimanpour, Shea. Exendin-4 enhances insulin-positive phenotype of human pluripotent stem cell-derived beta cells during transplantation. In preparation.
- Discipline:
- Engineering