arXiv subjects

Shun Yu

Publications and source records attributed to Shun Yu.

5 recordsLinked to original sources

arXiv2026

A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles

Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray scattering (SAXS) is an important characterization technique for LNPs, but recovering internal structure and size distribution from SAXS is an inverse problem with non-unique solutions. Realistic models are often too expensive for systematic exploration. We introduce a machine-learning-accelerated, differentiable framework for SAXS analysis of heterogeneous, polydisperse LNPs. The forward model combines a core-shell particle with a Gaussian random-field interior, a neural surrogate for the monodisperse SAXS map, and a differentiable layer integrating over particle-size distributions. The surrogate reduces prediction cost by four orders of magnitude, while differentiability enables large-scale multi-start fitting and ensemble identifiability analysis. Applied to synthetic and experimental MC3 LNP data, the framework shows that near-identical SAXS fits can arise from distinct parameter modes, with the experimental fits dominated by a trade-off between size-distribution and interior-structure parameters.

physics.comp-ph↗

arXiv2026

ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation

Accurate reconstruction of arbitrary-shaped long slender continuum bodies, such as guidewires, catheters and other soft continuum manipulators, is essential for accurate mechanical simulation. However, existing image-based reconstruction approaches often suffer from limited accuracy because they often underutilize camera geometry, or lack generality as they rely on rigid geometric assumptions that may fail for continuum robots with complex and highly deformable shapes. To address these limitations, we propose ACCURATE, a 3D reconstruction framework integrating an image segmentation neural network with a geometry-constrained topology traversal and dynamic programming algorithm that enforces global biplanar geometric consistency, minimizes the cumulative point-to-epipolar-line distance, and remains robust to occlusions and epipolar ambiguities cases caused by noise and discretization. Our method achieves high reconstruction accuracy on both simulated and real phantom datasets acquired using a clinical X-ray C-arm system, with mean absolute errors below 1.0 mm.

cs.RO↗

arXiv2023

An iterative algorithm for high-dimensional linear models with both sparse and non-sparse structures

Numerous practical medical problems often involve data that possess a combination of both sparse and non-sparse structures. Traditional penalized regularizations techniques, primarily designed for promoting sparsity, are inadequate to capture the optimal solutions in such scenarios. To address these challenges, this paper introduces a novel algorithm named Non-sparse Iteration (NSI). The NSI algorithm allows for the existence of both sparse and non-sparse structures and estimates them simultaneously and accurately. We provide theoretical guarantees that the proposed algorithm converges to the oracle solution and achieves the optimal rate for the upper bound of the $l_2$-norm error. Through simulations and practical applications, NSI consistently exhibits superior statistical performance in terms of estimation accuracy, prediction efficacy, and variable selection compared to several existing methods. The proposed method is also applied to breast cancer data, revealing repeated selection of specific genes for in-depth analysis.

stat.ME↗

arXiv2021

Machine Learning-Assisted Analysis of Small Angle X-ray Scattering

Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures. The analysis of experimental SAXS data involves mapping a rather simple data format to a vast amount of structural models. Despite various scientific computing tools to assist the model selection, the activity heavily relies on the SAXS analysts' experience, which is recognized as an efficiency bottleneck by the community. To cope with this decision-making problem, we develop and evaluate the open-source, Machine Learning-based tool SCAN (SCattering Ai aNalysis) to provide recommendations on model selection. SCAN exploits multiple machine learning algorithms and uses models and a simulation tool implemented in the SasView package for generating a well defined set of datasets. Our evaluation shows that SCAN delivers an overall accuracy of 95%-97%. The XGBoost Classifier has been identified as the most accurate method with a good balance between accuracy and training time. With eleven predefined structural models for common nanostructures and an easy draw-drop function to expand the number and types training models, SCAN can accelerate the SAXS data analysis workflow.

cs.LG↗

arXiv2015

Interaction of Human Serum Albumin with short Polyelectrolytes: A study by Calorimetry and Computer Simulation

We present a comprehensive study of the interaction of human serum albumin (HSA) with poly(acrylic acid) (PAA; number average degree of polymerization: 25) in aqueous solution. The interaction of HSA with PAA is studied in dilute solution as the function of the concentration of added salt (20 - 100 mM) and temperature (25 - 37$^{\circ}$C). Isothermal titration calorimetry (ITC) is used to analyze the interaction and to determine the binding constant and related thermodynamic data. It is found that only one PAA chain is bound per HSA molecule. The free energy of binding $\Delta G_b$ increases with temperature significantly. $\Delta G_b$ decreases with increasing salt concentration and is dominated by entropic contributions due to the release of bound counterions. Coarse-grained Langevin computer simulations treating the counterions in an explicit manner are used study the process of binding in detail. These simulations demonstrate that the PAA chains are bound in the Sudlow II site of the HSA. Moreover, $\Delta G_b$ is calculated from the simulations and found to be in very good agreement with the measured data. The simulations demonstrate clearly that the driving force of binding is the release of counterions in full agreement with the ITC-data.

cond-mat.soft↗