Evidence map›Paper›PMID 31552073›Full record

ArticleFrontiers in plant science2019

NegFluo, a Fast and Efficient Method to Determine Starch Granule Size and Morphology

Camille Vandromme, Angelina Kasprowicz, Adeline Courseaux, Dave Trinel, Maud Facon, Jean-Luc Putaux, Christophe D'Hulst, Fabrice Wattebled, Corentin Spriet

Abstract read
In one paragraph

Article in Frontiers in plant science, 2019. 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. Article
  3. 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

9 authors.

Camille VandrommeUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Angelina KasprowiczUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Adeline CourseauxUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Dave TrinelUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Maud FaconUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Jean-Luc PutauxUniv. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.
Christophe D'HulstUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Fabrice WattebledUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Corentin SprietUniv. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Starch granules that accumulate in the plastids of plants vary in size, shape, phosphate, or protein content according to their botanical origin. Depending on their size, the applications in food and nonfood industries differ. Being able to master starch granule size for a specific plant, without alteration of other characteristics (phosphate content, protein content, etc.), is challenging. The development of a simple and effective screening method to determine the size and shape of starch granules in a plant population is therefore of prime interest. In this study, we propose a new method, NegFluo, that combines negative confocal autofluorescence imaging in leaf and machine learning (ML)-based image analysis. It provides a fast, automated, and easy-to-use pipeline for both

Indexed as

Arabidopsisautofluorescenceconfocal fluorescence imagingmachine learningstarchstarch granule morphology

Identifiers

PMID31552073
PMCPMC6746253

What OpenQuestion holds

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Registered trials

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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.