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Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning
Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161767/ https://www.ncbi.nlm.nih.gov/pubmed/36961758 http://dx.doi.org/10.1107/S205225252300204X |
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author | Sun, Minghui Dong, Zheng Wu, Liyuan Yao, Haodong Niu, Wenchao Xu, Deting Chen, Ping Gupta, Himadri S. Zhang, Yi Dong, Yuhui Chen, Chunying Zhao, Lina |
author_facet | Sun, Minghui Dong, Zheng Wu, Liyuan Yao, Haodong Niu, Wenchao Xu, Deting Chen, Ping Gupta, Himadri S. Zhang, Yi Dong, Yuhui Chen, Chunying Zhao, Lina |
author_sort | Sun, Minghui |
collection | PubMed |
description | Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials. |
format | Online Article Text |
id | pubmed-10161767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-101617672023-05-06 Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning Sun, Minghui Dong, Zheng Wu, Liyuan Yao, Haodong Niu, Wenchao Xu, Deting Chen, Ping Gupta, Himadri S. Zhang, Yi Dong, Yuhui Chen, Chunying Zhao, Lina IUCrJ Research Papers Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials. International Union of Crystallography 2023-03-25 /pmc/articles/PMC10161767/ /pubmed/36961758 http://dx.doi.org/10.1107/S205225252300204X Text en © Minghui Sun et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Sun, Minghui Dong, Zheng Wu, Liyuan Yao, Haodong Niu, Wenchao Xu, Deting Chen, Ping Gupta, Himadri S. Zhang, Yi Dong, Yuhui Chen, Chunying Zhao, Lina Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title_full | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title_fullStr | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title_full_unstemmed | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title_short | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning |
title_sort | fast extraction of three-dimensional nanofiber orientation from waxd patterns using machine learning |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161767/ https://www.ncbi.nlm.nih.gov/pubmed/36961758 http://dx.doi.org/10.1107/S205225252300204X |
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