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Record W2559504107 · doi:10.3390/ijms17121964

Elevated O3 and TYLCV Infection Reduce the Suitability of Tomato as a Host for the Whitefly Bemisia tabaci

2016· article· en· W2559504107 on OpenAlexfundno aff
Hongying Cui, Yucheng Sun, Fajun Chen, Youjun Zhang, Feng Ge

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersZhejiang UniversityInstitute of GeneticsNational Natural Science Foundation of China
KeywordsWhiteflyHost (biology)BiologyHorticultureAgronomyEcology

Abstract

fetched live from OpenAlex

The effects of elevated atmospheric ozone (O3) levels on herbivorous insects have been well studied, but little is known about the combined effects of elevated O3 and virus infection on herbivorous insect performance. Using open-top chambers in the field, we determined the effects of elevated O3 and Tomato yellow leaf curl virus (TYLCV) infection on wild-type (Wt) tomato and 35S tomato (jasmonic acid (JA) defense-enhanced genotype) in association with whitefly, Bemisia tabaci Gennadius biotype B. Elevated O3 and TYLCV infection, alone and in combination, significantly reduced the contents of soluble sugars and free amino acids, increased the contents of total phenolics and condensed tannins, and increased salicylic acid (SA) content and the expression of SA-related genes in leaves. The JA signaling pathway was upregulated by elevated O3, but downregulated by TYLCV infection and O3 + TYLCV infection. Regardless of plant genotype, elevated O3, TYLCV infection, or O3 + TYLCV infection significantly decreased B. tabaci fecundity and abundance. These results suggest that elevated O3 and TYLCV infection, alone and in combination, reduce the nutrients available for B. tabaci, increase SA content and SA-related gene expression, and increase secondary metabolites, resulting in decreases in fecundity and abundance of B. tabaci in both tomato genotypes.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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