Optimization study of a clustering algorithm for cosmic-ray muon scattering tomography used in fast inspection

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

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A novel reconstruction algorithm based on density clustering for cosmic-ray muon scattering inspection

Nuclear Engineering and Technology ◽

10.1016/j.net.2021.01.014 ◽

2021 ◽

Author(s):

Linjun Hou ◽

Quanhu Zhang ◽

Jianqing Yang ◽

Xingfu Cai ◽

Qingxu Yao ◽

...

Keyword(s):

Cosmic Ray ◽

Reconstruction Algorithm ◽

Muon Scattering ◽

Density Clustering

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Verification of Spent Nuclear Fuel in Sealed Dry Storage Casks via Measurements of Cosmic-Ray Muon Scattering

Physical Review Applied ◽

10.1103/physrevapplied.9.044013 ◽

2018 ◽

Vol 9 (4) ◽

Cited By ~ 8

Author(s):

J. M. Durham ◽

D. Poulson ◽

J. Bacon ◽

D. L. Chichester ◽

E. Guardincerri ◽

...

Keyword(s):

Nuclear Fuel ◽

Spent Nuclear Fuel ◽

Cosmic Ray ◽

Muon Scattering ◽

Dry Storage

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Research on the Simulation of Radiographic Imaging Based on Cosmic-Ray Muon Scattering and the Optimization of PoCA

Volume 2: Nuclear Policy; Nuclear Safety, Security, and Cyber Security; Operating Plant Experience; Probabilistic Risk Assessments; SMR and Advanced Reactors ◽

10.1115/icone2020-16268 ◽

2020 ◽

Author(s):

Lin-jun Hou ◽

Quan-hu Zhang ◽

Yong-gang Huo ◽

Wen-ming Zuo ◽

Qi-fan Chen ◽

...

Keyword(s):

Image Reconstruction ◽

Cosmic Ray ◽

Optimization Method ◽

Experimental Models ◽

Nuclear Materials ◽

Radiographic Imaging ◽

Muon Scattering ◽

Simulation Experiments ◽

Imaging Quality ◽

Designed Experiments

Abstract Muon scattering tomography technology can be of great use in detecting nuclear materials under excellent shielding, thus distinguishing itself from traditional radiography technology. In this study, we firstly introduced the principle of this radiography technology and the rationale of PoCA algorithm used in image reconstruction. Then several experimental models were constructed with the help of Geant4 toolkit, and several sets of simulation experiments were carried out to test the performance of this technology with PoCA algorithm. In order to improve the imaging quality, we designed experiments to find out the cause of distorted image, and then proposed an optimization method. Finally, a quantity was created to present the imaging quality, and several experiments were carried out to validate the effect of this optimization method. The result showed that this method can significantly improve the performance of this algorithm.

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Application of muon tomography to fuel cask monitoring

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2018.0052 ◽

2018 ◽

Vol 377 (2137) ◽

pp. 20180052 ◽

Cited By ~ 1

Author(s):

Dan Poulson ◽

Jeff Bacon ◽

Matt Durham ◽

Elena Guardincerri ◽

C. L. Morris ◽

...

Keyword(s):

National Laboratory ◽

Model Fitting ◽

Atomic Charge ◽

Cosmic Ray ◽

Spent Fuel ◽

Muon Scattering ◽

Los Alamos National Laboratory ◽

Muon Tomography ◽

Term Monitoring

Long-term monitoring of spent fuel stored in dry cask storage is currently achieved through the use of seals and surveillance. Muon tomography can provide direct imaging that may be useful in cases where what is known as Continuity of Knowledge (CoK) has been lost using the former methods. Over the past several years, a team from Los Alamos National Laboratory has been studying the use of muon scattering and stopping to examine spent fuel in dry cask storage. Data taken on a partially loaded Westinghouse MC-10 fuel cask have demonstrated that muon scattering radiography can detect missing fuel assemblies. A model, validated by this data, shows that tomographic reconstructions of the fuel can be obtained in relatively short exposures. Model fitting algorithms have been developed for dealing with datasets with limited angular that appear to work well. Here we show that muon tomography can provide a fingerprint of a loaded fuel cask, because of its sensitivity to both the density and atomic charge of the spent fuel, and that it is sensitive to many diversion scenarios. This article is part of the Theo Murphy meeting issue ‘Cosmic-ray muography’.

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