We apply expert elicitation to assign informative prior to material flow analysis and conduct Bayesian inference for parameter and data noise learning.
Dong, Jiayuan, Jiankan Liao, Xun Huan, and Daniel Cooper. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference." Journal of Industrial Ecology 27, no. 4 (2023): 1105-1122.
We apply Bayesian inference to reduce network structure uncertainty on material flow analysis (MFA) and demonstrate the methodology through a case study on U.S. steel flow. In addition, we derive an input/output-based analysis to conduct decision-making based on the uncertainty results from MFA
Liao, Jiankan, Deng, Sidi, Xun Huan, and Daniel Cooper. "Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis." arXiv preprint arXiv:2501.05556 (2025).
During its trajectory, Wind spent a significant amount of time in the magnetotail, where its SupraThermal Ion Composition Spectrometer (STICS) measured the mass and mass per charge of protons, alpha particles, and heavy ions with an energy/charge ratio up to 226 keV/e. Although STICS originally aimed to measure the abundance of these ion species in the solar wind, its measurements within the magnetosphere from 1995 to 2002 help us identify preferential entry between the different solar wind ion species. This study statistically analyzes how the ratio between solar wind heavy ions and alpha particles (Heavies Solar Wind / He2+) varies for different upstream conditions and locations within the magnetosphere: northward vs. southward Interplanetary Magnetic Field (IMF), low vs. high solar wind density (Nsw), low vs. high solar wind dynamic pressure (PDyn), slow vs. fast solar wind (Vsw), and dawn vs. dusk. Our results indicate that the HeaviesSolar Wind enter the magnetosphere more efficiently than He2+ during northward IMF and that the Heavies Solar Wind / He2+ ratios decrease during high PDyn. In addition, the Heavies Solar Wind / He2+ ratios exhibit a dawn-dusk asymmetry, highly skewed towards the dawn side for all upstream cases likely due to charge-exchange processes.
Colón-Rodríguez, S., Liemohn, M. W., Raines, J. M, & Lepri, S.T. (2024). Solar wind heavy ions and alpha particles within Earth’s magnetosphere and their variability with upstream conditions. Journal of Geophysical Research Space Physics. In preparation.
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/
N. Lydick, J. Hu, and H. Deng, "Dimensional dependence of a molecular-polariton mode number," J. Opt. Soc. Am. B 41, C247-C253 (2024). https://doi.org/10.1364/JOSAB.524026
The research that produced this data tested how sleep loss impacted the phenomena of reactivation and replay, which occurs when recently-learned information is reactivated/replayed during post-learning sleep/rest.
Single molecule long read RNA/cDNA sequencing of TERT revealed 45 TERT mRNA variants including 13 known and 32 novel variants. Among the variants, TERT Delta 2-4, which lacks exons 2-4 but retains the original open reading frame, was selected for further study. Induced pluripotent stem cells and cancer cells express higher levels of TERT Delta 2-4 compared to primary human bronchial epithelial cells. Overexpression of TERT Delta 2-4 enhanced clonogenicity and resistance to cisplatin-induced apoptosis. Knockdown of endogenous TERT Delta 2-4 in Calu-6 cells reduced clonogenicity and resistance to cisplatin. Our results suggest that TERT Delta 2-4 enhances cancer cells’ resistance to intrinsic apoptosis. RNA sequencing following knockdown of Delta 2-4 TERT indicates that translation is downregulated and that mitochondrial related proteins are upregulated compared to controls.
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.
Images were collected as part of a project investigating the interpretation of BMP signaling dynamics by differentiating human pluripotent stem cells.
Image files are in the proprietary Imaris (.ims) file format. MATLAB and Python code for image processing and quantification is provided with the data and at https://github.com/seth414/HeemskerkLabMethods. Processed data originally published in Teague et al., 2024 (see below).
Teague, S., Primavera, G., Chen, B. et al. Time-integrated BMP signaling determines fate in a stem cell model for early human development. Nat Commun 15, 1471 (2024). https://doi.org/10.1038/s41467-024-45719-9
The goal of this project is to relate properties of nanowire networks to their structure. The structure of these networks was determined from electron and atomic force microscopy, which were used as the basis for property predictions. Properties include sheet resistance, conductive anisotropy, absorption spectra, and current capacity.
