ArticlePlants (Basel, Switzerland)2022
Comparing Machine Learning and Binary Thresholding Methods for Quantification of Callose Deposits in the Citrus Phloem.
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- NPR1 suppresses Candidatus Liberibacter asiaticus-induced callose and reactive oxygen species accumulation.Plant physiology · 2025Article
- Fight smarter, not harder: NPR1-mediated immune balance in citrus greening disease.Plant physiology · 2025Article
- Exploring the biochemical and molecular mechanisms that contribute to Huanglongbing (HLB) tolerance in Citrus australis hybrids.BMC genomics · 2025Article
- Reliable detection and quantification of plasmodesmal callose inbioRxiv : the preprint server for biology · 2023Article
- Insights into the mechanism of Huanglongbing tolerance in the Australian finger lime (Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Callose is a polysaccharide that can be fluorescently stained to study many developmental and immune functions in plants. High-throughput methods to accurately gather quantitative measurements of callose from confocal images are useful for many applications in plant biology. Previous callose quantification methods relied upon binary local thresholding, which had the disadvantage of not being able to differentiate callose in conditions with low contrast from background material. Here, a measurement approach that utilizes the Ilastik supervised machine learning imagery data collection software is described. The Ilastik software method provided superior efficiency for acquiring counts of callose deposits. We also determined the accuracy of these methods as compared to manual counts. We demonstrate that the automated software methods are both good predictors of manual counts, but that the Ilastik counts are significantly closer. Researchers can use this information to guide their choice of method to quantify callose in their work.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.