Evidence map›Paper›PMID 37737204›Full record

ArticleNature communications2023

Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation.

Reiko Akiyama, Takao Goto, Toshiaki Tameshige, Jiro Sugisaka, Ken Kuroki, Jianqiang Sun, Junichi Akita, Masaomi Hatakeyama, Hiroshi Kudoh, Tanaka Kenta and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  5. 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

16 authors.

Reiko AkiyamaDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-6702-5101
Takao GotoResearch and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan.
Toshiaki TameshigeKihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan.ORCID http://orcid.org/0000-0001-9959-3800
Jiro SugisakaKihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan.
Ken KurokiDepartment of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.ORCID http://orcid.org/0000-0002-2096-2747
Jianqiang SunResearch Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki, 305-8517, Japan.ORCID http://orcid.org/0000-0002-3438-3199
Junichi AkitaDepartment of Electric and Computer Engineering, Kanazawa University, Kakuma, Kanazawa, 920-1192, Japan.
Masaomi HatakeyamaDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Hiroshi KudohCenter for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, 520-2113, Japan.ORCID http://orcid.org/0000-0001-9777-4886
Tanaka KentaSugadaira Research Station, Mountain Science Center, University of Tsukuba, 1278-294 Sugadaira-kogen, Ueda, 386-2204, Japan.
Aya TonouchiResearch and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan.
Yuki ShimaharaResearch and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan.
Jun SeseArtificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan.ORCID http://orcid.org/0000-0003-3495-4382
Natsumaro KutsunaResearch and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan.
Rie Shimizu-InatsugiDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland. rie.inatsugi@ieu.uzh.ch.ORCID http://orcid.org/0000-0002-9899-591X
Kentaro K ShimizuDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland. kentaro.shimizu@uzh.ch.ORCID http://orcid.org/0000-0002-6483-1781

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".

Indexed as

AnthocyaninsArabidopsisDiploidyHumansMachine LearningPolyploidySeasonsAnthocyanins

Identifiers

PMID37737204
PMCPMC10517152

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.