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Record W2975857986 · doi:10.1002/lno.11329

A reduction‐dependent copper uptake pathway in an oceanic diatom

2019· article· en· W2975857986 on OpenAlexafffund
Liangliang Kong, Neil M. Price

Bibliographic record

VenueLimnology and Oceanography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCopperChemistryEthylenediaminetetraacetic acidDiatomExtracellularInorganic chemistryChelationSeawaterPhytoplanktonNuclear chemistryEnvironmental chemistryBiochemistryBiologyBotanyNutrientEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Copper(II) is reduced to Cu(I) extracellularly by marine and freshwater phytoplankton, but its biological significance is not firmly established. We studied the relationship between Cu(II) reduction and uptake in Thalassiosira oceanica , a diatom that was recently shown to possess functional copper uptake transporters (CTRs) that take up Cu(I). Inorganic and organic complexes of Cu(II) were reduced directly by reactions at the cell surface in proportion to Cu(II)‐ligand reduction potential. The rates of reduction were enhanced twofold in Cu‐limited cells, suggesting reduction was regulated by Cu nutritional state. Suppressing Cu(II) reduction caused a decrease in Cu uptake rate by 97% and addition of a Cu(I) complexing agent completely inhibited cell division and reduced Cu quota when Cu concentration was growth limiting. Thus, Cu(II) reduction was an obligatory first step in Cu uptake. Cu(II) reduction rate and growth rate of T. oceanica were proportional to Cu–ethylenediaminetetraacetic acid concentration and independent of inorganic Cu concentration in bulk solution. The results suggest that Cu(II) bound to organic ligands was reduced by extracellular cupric reductases and subsequently internalized. This reduction‐dependent uptake pathway may enable diatoms to use naturally occurring Cu(II) organic complexes in the sea.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.189
Teacher spread0.183 · 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.

Study designObservational
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

Citations29
Published2019
Admission routes2
Has abstractyes

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