Cargando…
Particle morphomics by high-throughput dynamic image analysis
A novel omics-like method referred to as “particle morphomics” has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors o...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610128/ https://www.ncbi.nlm.nih.gov/pubmed/31270428 http://dx.doi.org/10.1038/s41598-019-46062-6 |
_version_ | 1783432445547249664 |
---|---|
author | Sun, Youmin Cai, Zhengqing Fu, Jie |
author_facet | Sun, Youmin Cai, Zhengqing Fu, Jie |
author_sort | Sun, Youmin |
collection | PubMed |
description | A novel omics-like method referred to as “particle morphomics” has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors of each particle including equivalent diameter, sphericity, aspect ratio and convexity were extracted as the “particle morphome”. Various multivariate analyses were adopted to process the high-throughput data of particle morphome including analyses of alpha and beta diversities, similarity, correlation, network, redundancy, discretion and principal coordinate. The outcome of particle morphomics could estimate the morphological diversity and sketch the profile of morphological structure, which aided to develop a morphological fingerprint for specific particle samples. The distribution and properties of particle assemblages of specific morphology could also be evaluated by selecting particles with respect to filter criteria. More importantly, the particle morphomics may be extended to investigate and explain the biogeochemical and environmental processes involved with particle morphology if linked with external variables. |
format | Online Article Text |
id | pubmed-6610128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66101282019-07-14 Particle morphomics by high-throughput dynamic image analysis Sun, Youmin Cai, Zhengqing Fu, Jie Sci Rep Article A novel omics-like method referred to as “particle morphomics” has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors of each particle including equivalent diameter, sphericity, aspect ratio and convexity were extracted as the “particle morphome”. Various multivariate analyses were adopted to process the high-throughput data of particle morphome including analyses of alpha and beta diversities, similarity, correlation, network, redundancy, discretion and principal coordinate. The outcome of particle morphomics could estimate the morphological diversity and sketch the profile of morphological structure, which aided to develop a morphological fingerprint for specific particle samples. The distribution and properties of particle assemblages of specific morphology could also be evaluated by selecting particles with respect to filter criteria. More importantly, the particle morphomics may be extended to investigate and explain the biogeochemical and environmental processes involved with particle morphology if linked with external variables. Nature Publishing Group UK 2019-07-03 /pmc/articles/PMC6610128/ /pubmed/31270428 http://dx.doi.org/10.1038/s41598-019-46062-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sun, Youmin Cai, Zhengqing Fu, Jie Particle morphomics by high-throughput dynamic image analysis |
title | Particle morphomics by high-throughput dynamic image analysis |
title_full | Particle morphomics by high-throughput dynamic image analysis |
title_fullStr | Particle morphomics by high-throughput dynamic image analysis |
title_full_unstemmed | Particle morphomics by high-throughput dynamic image analysis |
title_short | Particle morphomics by high-throughput dynamic image analysis |
title_sort | particle morphomics by high-throughput dynamic image analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610128/ https://www.ncbi.nlm.nih.gov/pubmed/31270428 http://dx.doi.org/10.1038/s41598-019-46062-6 |
work_keys_str_mv | AT sunyoumin particlemorphomicsbyhighthroughputdynamicimageanalysis AT caizhengqing particlemorphomicsbyhighthroughputdynamicimageanalysis AT fujie particlemorphomicsbyhighthroughputdynamicimageanalysis |