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Record W2475040791 · doi:10.5539/ijb.v8n3p66

Molecular Characterization of a Catalase Gene (VsCat) from Vicia sativa

2016· article· en· W2475040791 on OpenAlexvenueno aff
Saeid Abu-Romman

Bibliographic record

VenueInternational Journal of Biology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyComplementary DNAOpen reading frameAbiotic stressVicia fabaPeroxisomal targeting signalGenePeroxisomeMolecular biologyBiochemistryPeptide sequenceBotany

Abstract

fetched live from OpenAlex

In plants, catalases are encoded by a multigene family and are predominantly localized in the peroxisomes and glyoxysomes for scavenging H<sub>2</sub>O<sub>2</sub>. A full-length cDNA encoding <em>Catalase</em> (<em>VsCat</em>) was isolated from <em>Vicia sativa</em> using RT-PCR. The cDNA consisted of 1485 bp open reading frame (ORF) encoding a 494 amino acid polypeptide with a predicted molecular mass of 57.03 kDa and an estimated pI of 6.56. The predicted protein was highly homologues to other catalases from legume plants and contained typical catalytic active site, calmodulin binding domain, and internal peroxisomal targeting signal. Phylogenetic analysis revealed that VsCat is evolutionary close to faba bean and pea Cat1. The expression patterns of <em>VsCat</em> under different abiotic stresses and exogenous phytohormones were determined by quantitative RT-PCR<strong>. </strong>Compared to control plants, <em>VsCat</em> was differentially up-regulated in response to abiotic stresses and phytohormones. The expression analysis suggested that <em>VsCat</em> is involved in different abiotic stress responses. In future experiment, transgenic plants overexpressing <em>VsCat</em> might be a good choice to increase tolerance of forage plants 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)0.000
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 score0.316
Threshold uncertainty score0.243

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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