Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Chu, Yanwu; Luo, Yu; Chen, Feng; Zhao, Chengwei; Gong, Tiancheng; Wang, Yanqing; Guo, Lianbo; Hong, Minghui

doi:10.1016/j.isci.2023.106173

iScience

2023

DOI: 10.1016/j.isci.2023.106173

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Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Yanwu Chu¹,

Yu Luo²,

Feng Chen³

et al.

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2024

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Cited by 3 publications

(1 citation statement)

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“…We found that 73.07% of SNPs were located in intergenic regions, while only 88 908 SNPs were annotated as missense variants (Table 1). Currently, more and more research indicated that a polygenic basis plays a crucial role in regulating the important economic traits of domestic animals (Chu et al, 2023). The traditional strategy for analyzing selection signatures may be unable to accurately capture these correlated loci owing to their small effects (Vitti et al, 2013).…”

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Genetic basis of phenotypic convergence in pig terminal sires using pathway‐based selection signature detection methods

Li,

Peng

et al. 2024

Animal Genetics

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The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway‐based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high‐quality SNPs from whole‐genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway‐based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway‐based gene network analysis.

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Genetic basis of phenotypic convergence in pig terminal sires using pathway‐based selection signature detection methods

Li,

Peng

et al. 2024

Animal Genetics

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Effects of feature engineering on the robustness of laser-induced breakdown spectroscopy for industrial steel classification

Jeon,

Keum,

Lee

et al. 2024

Spectrochimica Acta Part B: Atomic Spectroscopy

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High-accuracy quantification of soil elements by laser-induced breakdown spectroscopy based on PCA-GS-ELM

Qu,

Li,

Sun

et al. 2024

J. Anal. At. Spectrom.

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Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning

Cited by 3 publications

References 36 publications

Genetic basis of phenotypic convergence in pig terminal sires using pathway‐based selection signature detection methods

Genetic basis of phenotypic convergence in pig terminal sires using pathway‐based selection signature detection methods

Effects of feature engineering on the robustness of laser-induced breakdown spectroscopy for industrial steel classification

High-accuracy quantification of soil elements by laser-induced breakdown spectroscopy based on PCA-GS-ELM

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