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Record W2109583102 · doi:10.1094/phyto-99-2-0167

Ultrastructural Alterations in <i>Fusarium sambucinum</i> and <i>Heterobasidion annosum</i> Treated with Aluminum Chloride and Sodium Metabisulfite

2009· article· en· W2109583102 on OpenAlexaff
Tyler J. Avis, Danny Rioux, Mélissa Simard, Mélanie Michaud, Russell J. Tweddell

Bibliographic record

VenuePhytopathology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsUltrastructureSodium metabisulfiteHeterobasidion annosumBiologyConidiumHyphaBiophysicsMicrobiologyBotany

Abstract

fetched live from OpenAlex

Aluminum chloride (AlCl(3)) and sodium metabisulfite (Na(2)S(2)O(5)) have received increasing attention as antifungal agents for the control of plant diseases. In an effort to understand their toxic action on fungi, ultrastructural changes and membrane damage in Fusarium sambucinum (Ascomycota) and Heterobasidion annosum (Basidiomycota) in response to salt exposure was investigated using transmission electron microscopy. Conidial membrane damage was quantified using SYTOX Green stain, which only enters altered membranes. The results showed that mortality of the conidia was generally closely associated with SYTOX stain absorption in F. sambucinum treated with Na(2)S(2)O(5) and in H. annosum treated with AlCl(3) or Na(2)S(2)O(5), suggesting that these salts cause membrane alterations. For both fungi, ultrastructural alterations in conidia treated with AlCl(3) and Na(2)S(2)O(5) included membrane retraction, undulation, and invagination. At higher concentrations or exposure periods to the salts, loss of membrane integrity, cytoplasmic leakage, and cell rupture were observed. Ultrastructural alterations and increased SYTOX stain absorption in salt-treated conidia appear consistent with a mode of action where AlCl(3) and Na(2)S(2)O(5) alter membrane integrity and permeability.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.301

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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 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

Citations25
Published2009
Admission routes1
Has abstractyes

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