Evidence map›Paper›PMID 35720539›Full record

ArticleFrontiers in plant science2022

Extreme Low-Temperature Stress Affects Nutritional Quality of Amino Acids in Rice.

Min Kang, Gurong Liu, Yaowen Zeng, Jia Zhou, Jiangyi Shi, Liang Tang, Leilei Liu, Weixing Cao, Yan Zhu, Bing Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 10% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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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

10 authors at 1 institution in 1 country.

Min KangKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Gurong LiuKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Yaowen ZengKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Jia ZhouKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Jiangyi ShiKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Liang TangKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Leilei LiuKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Weixing CaoKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Yan ZhuKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Bing LiuKey Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Ministry of Education, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China.
Nanjing Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global climate change has increased the frequency of extreme climate events, and their effects on the nutritional quality, especially on amino acids in rice, have not been quantified. The data from a 3-year low temperature stress (LTS) experiment including two rice varieties (Huaidao 5 and Nanjing 46), seven minimum/maximum temperature levels (one optimal 21/27°C and six LTS levels from 17/23 to 6/12°C), and three LTS durations (3, 6, and 9 days) after flowering, revealed significant interactive effects of LTS at different stages, durations, and temperature levels on the content and accumulation of amino acids. LTS increased rice total amino acid content, while decreasing its accumulation, with higher sensitivities to LTS at the flowering stage than at the grain filling stage. In most treatments, the lysine (the first limiting amino acid) and phenylalanine content were increased under LTS at early and peak flowering stages but decreased at the grain filling stage in both varieties, and only leucine content was increased at all three stages after flowering, while the content of other essential amino acids differed among the two varieties. With an increase of 1°C·d per day in the accumulated cold degree days, the relative content of the essential amino acids was increased by 0.01-0.41%, depending on the rice variety and growth stage. Our results suggest that LTS can improve nutritional quality of amino acids of rice grains in terms of amino acids content, especially at flowering stage. These results provide critical insights for assessing the potential impact of extreme climates on the nutrient quality of rice under future climate change.

Indexed as

amino acidclimate changeflowering stagegrain filling stagelow-temperature stressrice

Identifiers

PMID35720539
PMCPMC9201379
OpenAlexW4281721535

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.