Evidence map›Paper›PMID 40522339›Full record

ArticlePlant reproduction2025

Semi-automated high content analysis of pollen performance using tubetracker.

Sorel V Yimga Ouonkap, Yahir Oseguera, Bryce Okihiro, Mark A Johnson

Abstract read
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In one paragraph

Article in Plant reproduction, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Sorel V Yimga OuonkapDepartment of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA.
Yahir OsegueraDepartment of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA.
Bryce OkihiroDepartment of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA.
Mark A JohnsonDepartment of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA. Mark_Johnson_1@brown.edu.ORCID 0000-0002-8508-2724

Funding

Division of Integrative Organismal Systems IOS-1939255National Institute of Food and Agriculture 2020-67013-30907
6 · The paper itself

Abstract

key messageTubeTracker provides a method to partially automate analysis of pollen tube growth using live imaging. Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and maintenance of pollen tube integrity. TubeTracker integrates manual and automatic image processing routines and the graphical interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato.

Indexed as

Image Processing, Computer-AssistedPollenPollen TubeSolanum lycopersicumGerminationSoftwareAutomated analysisLive imagingPollen tube growthTomato

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.