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Record W2121782169 · doi:10.2298/abs1201135s

Expression of small heat shock proteins and heat tolerance in potato (Solanum tuberosum L.)

2012· article· en· W2121782169 on OpenAlexaff
Jelena Savić, Ivana Dragićević, Danijel Pantelić, Jasmina Oljača, Ivana Momčilović

Bibliographic record

VenueArchives of Biological Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsInstitute for Biological Sciences
FundersScience and Engineering Research Board
KeywordsSolanum tuberosumHeat shock proteinIn vitroCultivarBiologyHeat stressBotanyHorticultureBiochemistryGene

Abstract

fetched live from OpenAlex

We have examined the correlation between heat tolerance and small heat shock protein (sHSP) expression under heat stress conditions in potato (Solanum tuberosum L.). The relative heat tolerance of nine potato cultivars grown under greenhouse conditions was determined using the electrolyte leakage assay (ELA), a standard quantitative assay for heat tolerance. Three cultivars differing in heat tolerance were selected and designated as heat-tolerant (?Laura?), moderately sensitive (?Liseta?) and heat-sensitive (?Agria?) genotypes. The expression of cytosolic HSP18 and chloroplast HSP21 was analyzed at the protein level in the leaves of selected cultivars, both ex vitro- and in vitro-grown, after heat stress or control treatment. Immunoblot analysis revealed heat-induced HSP18 and HSP21 expression in all examined genotypes. A similar pattern of examined sHSP expression was observed ex vitro and in vitro: heat-tolerant ?Laura? accumulated higher levels of both HSP18 and HSP21 compared to heat-sensitive ?Liseta? and ?Agria?. Our results indicate that ELA combined with immunoblot analysis of sHSP accumulation under HS conditions, might be considered as a reliable procedure in screening potato genotypes for heat tolerance. To our knowledge, this is the first study where sHSP expression between ex vitro- and in vitro-grown potato plants was compared.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.359

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 studies0.0000.001
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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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

Citations28
Published2012
Admission routes1
Has abstractyes

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