Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
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
_version_ 1785037560448286720
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
work_keys_str_mv AT sunminghui fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT dongzheng fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT wuliyuan fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT yaohaodong fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT niuwenchao fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT xudeting fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT chenping fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT guptahimadris fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT zhangyi fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT dongyuhui fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT chenchunying fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning
AT zhaolina fastextractionofthreedimensionalnanofiberorientationfromwaxdpatternsusingmachinelearning