AFMnanoSALQ: An Accurate Detection Framework for Semi-Automatic Labeling and Quantitative Analysis of α-Hemolysin Nanopores Using Intensity-Height Cues in HS-AFM Data Article Swipe
Thi-Ngoc-Truc Nguyen
,
Ngoc Quoc Ly
,
Ngan Thi Phuong Le
,
Hoang Duc Nguyen
,
Kien Xuan Ngo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.26.640237
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.26.640237
High-Speed Atomic Force Microscopy (HS-AFM) enables imaging of biological structures and dynamics with nanometer spatial and millisecond temporal resolution. AFM images contain three-dimensional (3D) surface information, comprising two-dimensional (2D) lateral (x-y) and one-dimensional (1D) height (z) encoded in pixel intensity. This dynamic structure poses significant challenges for instance boundary detection and morphological analysis. To address this, we develop AFMnanoSALQ, a feature-driven computational framework for semi-automatic labeling and quantitative (SALQ) detection and morphological measurement of HS-AFM data. Unlike conventional methods that rely solely on either visual or geometric features for 2D boundary detection, AFM- nanoSALQ integrates both to extract 3D morphology. It requires neither annotated data nor intensive training, enabling fast deployment at minimal cost. With performance comparable to typical deep-learning models, AFMnanoSALQ facilitates semi-automatic labeling, making it a practical tool for preliminary data inspection and accelerating the creation of training datasets. As a case study, we focus on α-hemolysin (αHL), a β-barrel pore-forming toxin secreted by Staphylococcus aureus , using both synthetic and experimental AFM data. AFMnanoSALQ provides a foundation for future deep learning studies, enabling both dataset generation and cross-validation between feature-driven and data-driven approaches.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.02.26.640237
- https://www.biorxiv.org/content/biorxiv/early/2025/03/02/2025.02.26.640237.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408084912
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Raw OpenAlex JSON
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https://openalex.org/W4408084912Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.02.26.640237Digital Object Identifier
- Title
-
AFMnanoSALQ: An Accurate Detection Framework for Semi-Automatic Labeling and Quantitative Analysis of α-Hemolysin Nanopores Using Intensity-Height Cues in HS-AFM DataWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-02Full publication date if available
- Authors
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Thi-Ngoc-Truc Nguyen, Ngoc Quoc Ly, Ngan Thi Phuong Le, Hoang Duc Nguyen, Kien Xuan NgoList of authors in order
- Landing page
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https://doi.org/10.1101/2025.02.26.640237Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/03/02/2025.02.26.640237.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/03/02/2025.02.26.640237.full.pdfDirect OA link when available
- Concepts
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Nanopore, Atomic force microscopy, Materials science, Nanotechnology, Image processing, Computer science, Image (mathematics), Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.measurement | 73 |
| abstract_inverted_index.millisecond | 17 |
| abstract_inverted_index.morphology. | 100 |
| abstract_inverted_index.performance | 116 |
| abstract_inverted_index.preliminary | 132 |
| abstract_inverted_index.resolution. | 19 |
| abstract_inverted_index.significant | 45 |
| abstract_inverted_index.AFMnanoSALQ, | 59 |
| abstract_inverted_index.accelerating | 136 |
| abstract_inverted_index.conventional | 78 |
| abstract_inverted_index.experimental | 164 |
| abstract_inverted_index.information, | 26 |
| abstract_inverted_index.pore-forming | 153 |
| abstract_inverted_index.quantitative | 68 |
| abstract_inverted_index.α-hemolysin | 149 |
| abstract_inverted_index.computational | 62 |
| abstract_inverted_index.deep-learning | 120 |
| abstract_inverted_index.morphological | 52, 72 |
| abstract_inverted_index.Staphylococcus | 157 |
| abstract_inverted_index.feature-driven | 61, 183 |
| abstract_inverted_index.semi-automatic | 65, 124 |
| abstract_inverted_index.one-dimensional | 33 |
| abstract_inverted_index.two-dimensional | 28 |
| abstract_inverted_index.cross-validation | 181 |
| abstract_inverted_index.three-dimensional | 23 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.7869183 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |