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Record W4289890776 · doi:10.5539/jps.v11n1p19

In vitro Bioassay of Allelopathic Activities of Soybean, and Three Isoflavones, Using Protoplast Co-culture Method with Digital Image Analysis

2022· article· en· W4289890776 on OpenAlexvenueno aff
Hamako Sasamoto, Yosuke Kobayashi, Tomoya Oyanagi, Yutaka Sasamoto

Bibliographic record

VenueJournal of Plant Studies · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsnot available
FundersResearch Institute for Sustainable Humanosphere, Kyoto University
KeywordsAllelopathyGenistinDaidzeinDaidzinIsoflavonesGenisteinProtoplastBiologyBotanyBioassaySeedlingGerminationBiochemistry

Abstract

fetched live from OpenAlex

Inhibitory allelopathic activities of dried leaves of potted plants and protoplasts of Glycine max (soybean) cv. Okuhara-wase were studied using two in vitro bioassay methods. Strong inhibition (90%) of growth of recipient lettuce root was obtained with 50 mg soybean leaves using the lettuce seedling growth test (sandwich method), and moderate inhibition (65%) of lettuce protoplast division was obtained with 100 × 103 mL-1 protoplasts of etiolated soybean seedlings using the protoplast co-culture method with digital image analysis (DIA-PP method). Three isoflavones, genistein, genistin (genistein-7-O-glucoside), and daidzin (daidzein-7-O-glucoside) were investigated as putative allelochemicals using the DIA-PP method. The three isoflavones at 100 µM showed early crystal accumulation in lettuce protoplasts and inhibited lettuce protoplast division by 100%. Genistein showed the strongest inhibition. The results were compared and discussed with the previous reports on the invader leguminous plant, Kudzu, and two isoflavones, daidzein and Kudzu-specific puerarin (daidzein-6-C-glucoside), and other allelopathic plants and their putative allelochemicals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.285
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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