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Record W2892722616 · doi:10.1111/jpi.12527

Direct visualization of location and uptake of applied melatonin and serotonin in living tissues and their redistribution in plants in response to thermal stress

2018· article· en· W2892722616 on OpenAlexafffund
Lauren A. E. Erland, Adam B. Yasunaga, Isaac T. S. Li, Susan J. Murch, Praveen K. Saxena

Bibliographic record

VenueJournal of Pineal Research · 2018
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British ColumbiaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMelatoninSerotoninBiologyCell biologyPineal glandSerotonergicBiophysicsReceptorBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

Melatonin and serotonin are important phytochemicals enabling plants to redirect growth in response to environmental stresses. Despite much research on their biosynthetic routes, localization of their biosynthetic enzymes and recent identification of a phytomelatonin receptor, localization of the molecules themselves has to date not been possible. Elucidation of their locations in living tissues can provide an effective tool to facilitate indolamine research across systems including both plants and animals. In this study, we employed a novel technique, quantum dot nanoparticles, to directly visualize melatonin and serotonin in axenic roots. Melatonin was absorbed through epidermal cells, travelled laterally, and accumulated in endodermal and rapidly dividing pericycle cells. Serotonin was absorbed by cells proximal to the crown with rapid polar movement toward the root tip. Thermal stress disrupted localization and dispersed melatonin and serotonin across cells. These data demonstrate the natural movement of melatonin and serotonin in roots directing cell growth and suggest that plants have a mechanism to disperse the indolamines throughout tissues as antioxidants in response to environmental stresses.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.486
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.051
GPT teacher head0.367
Teacher spread0.316 · 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

Citations84
Published2018
Admission routes2
Has abstractyes

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