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Chen, Peng

Publications and source records attributed to Chen, Peng.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

Here we propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in ~ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.

42 ENGINEERING↗
DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗
DOE OSTI2021

Structural basis for CSPG4 as a receptor for TcdB and a therapeutic target in Clostridioides difficile infection

C. difficile is a major cause of antibiotic-associated gastrointestinal infections. Two C. difficile exotoxins (TcdA and TcdB) are major virulence factors associated with these infections, and chondroitin sulfate proteoglycan 4 (CSPG4) is a potential receptor for TcdB, but its pathophysiological relevance and the molecular details that govern recognition remain unknown. Here, we determine the cryo-EM structure of a TcdB–CSPG4 complex, revealing a unique binding site spatially composed of multiple discontinuous regions across TcdB. Mutations that selectively disrupt CSPG4 binding reduce TcdB toxicity in mice, while CSPG4-knockout mice show reduced damage to colonic tissues during C. difficile infections. We further show that bezlotoxumab, the only FDA approved anti-TcdB antibody, blocks CSPG4 binding via an allosteric mechanism, but it displays low neutralizing potency on many TcdB variants from epidemic hypervirulent strains due to sequence variations in its epitopes. In contrast, a CSPG4-mimicking decoy neutralizes major TcdB variants, suggesting a strategy to develop broad-spectrum therapeutics against TcdB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Healing of nanocracks by collision cascades in nickel

We use molecular dynamics simulations to investigate the interaction between collision cascades and pre-existing nano-cracks in nickel (Ni). We find that collision cascades cause the cracks to heal whenever a cascade initiates a thermal spike whose core overlaps with the crack. Moreover, crack healing affects the distribution of radiation-induced defects generated during a collision cascade: defect clusters, such as dislocations and stacking fault tetrahedra, dominate in the presence of crack healing, while isolated point defects dominate in its absence. Furthermore, this work indicates that irradiation may reduce the number of pre-existing crack-like flaws in metallic components, potentially improving their mechanical resilience.

36 MATERIALS SCIENCE↗