Evidence map›Paper›PMID 38740920›Full record

ArticleScientific reports2024

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.

Shufan Zhang, Yue Wu, Michael Skaro, Jia-Hwei Cheong, Amanda Bouffier-Landrum, Isaac Torrres, Yinping Guo, Lauren Stupp, Brooke Lincoln, Anna Prestel and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shufan ZhangInstitute of Bioinformatics, University of Georgia, Athens, GA, USA.
Yue WuInstitute of Bioinformatics, University of Georgia, Athens, GA, USA.
Michael SkaroInstitute of Bioinformatics, University of Georgia, Athens, GA, USA.
Jia-Hwei CheongGenetics Department, University of Georgia, Athens, GA, USA.
Amanda Bouffier-LandrumInstitute of Bioinformatics, University of Georgia, Athens, GA, USA.
Isaac TorrresInstitute of Bioinformatics, University of Georgia, Athens, GA, USA.
Yinping GuoGenetics Department, University of Georgia, Athens, GA, USA.
Lauren StuppGenetics Department, University of Georgia, Athens, GA, USA.
Brooke LincolnGenetics Department, University of Georgia, Athens, GA, USA.
Anna PrestelGenetics Department, University of Georgia, Athens, GA, USA.
Camryn FeltGenetics Department, University of Georgia, Athens, GA, USA.
Sedona SpannSchool of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA.
Abhyuday MandalStatistics Department, University of Georgia, Athens, GA, USA.
Nancy JohnsonSchool of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA.
Jonathan ArnoldGenetics Department, University of Georgia, Athens, GA, USA. arnold@uga.edu.

Funding

Division of Biological Infrastructure NSF DBI-1946937U.S. Department of Energy DE-SC0021386
6 · The paper itself

Abstract

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

Indexed as

MycorrhizaePlant RootsSorghumLinear ModelsNeural Networks, ComputerSymbiosisArbuscular mycorrhizal fungiComputer visionMask R-CNNMixed linear models

Identifiers

PMID38740920
PMCPMC11091061

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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.