ArticleFrontiers in plant science2024
Efficiently recording and processing data from arbuscular mycorrhizal colonization assays using AMScorer and AMReader.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Novel imaging approaches for visualizing root-mycorrhizal fungal interactions.Journal of experimental botany · 2026Review
- From manual scoring to machine learning: recent developments in image-based arbuscular mycorrhizal fungi root quantification.Mycorrhiza · 2026Review
- Gibberellin biosynthesis inFrontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arbuscular mycorrhizal (AM) fungi engage with land plants in a widespread, mutualistic endosymbiosis which provides their hosts with increased access to nutrients and enhanced biotic and abiotic stress resistance. The potential for reducing fertiliser use and improving crop resilience has resulted in rapidly increasing scientific interest. Microscopic quantification of the level of AM colonization is of fundamental importance to this research, however the methods for recording and processing these data are time-consuming and tedious. In order to streamline these processes, we have developed AMScorer, an easy-to-use Excel spreadsheet, which enables the user to record data rapidly during from microscopy-based assays, and instantly performs the subsequent data processing steps. In our hands, AMScorer has more than halved the time required for data collection compared to paper-based methods. Subsequently, we developed AMReader, a user-friendly R package, which enables easy visualization and statistical analyses of data from AMScorer. These tools require only limited skills in Excel and R, and can accelerate research into AM symbioses, help researchers with variable resources to conduct research, and facilitate the storage and sharing of data from AM colonization assays. They are available for download at https://github.com/EJarrattBarnham/AMReader, along with an extensive user manual.
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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.