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Record W4404202211 · doi:10.1016/j.aac.2024.11.001

Migrated silicon dioxide nanoparticles activates the rice immunity for systemic resistance against two pathogens

2024· article· en· W4404202211 on OpenAlexaff
Nawei Tan, Yi Xu, Jingyue Wang, Bin Yuan, Heqiang Huo, Wen‐Xiu Qiu, Ying Zhou

Bibliographic record

VenueAdvanced Agrochem · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsSilicon dioxideImmunityResistance (ecology)MicrobiologyBiologyMaterials scienceImmune systemImmunologyEcologyMetallurgy

Abstract

fetched live from OpenAlex

Rice, the world's primary staple food, is under severe threat from several devastating diseases. To sustainably management rice diseases, developing safe, environmentally friendly alternatives urgently need to be developed. In this study, we synthesized two silicon dioxide nanoparticles, spherical mesoporous silica nanoparticles (MSNs) and virus-like mesoporous silica nanoparticles (VMSNs), and we performed a resistance assay on rice for two major diseases, bacterial blight caused by Xanthomonas oryzae pv. oryzae and sheath blight caused by Rhizoctonia solani . Compared to the control, the two nanoparticle treatments increased rice resistance, with VMSNs exhibiting the highest efficacy in controlling these two diseases, causing a shorter lesion length than those plants treated by MSNs. Coincidentally, the foliar application of VMSNs activates a higher expression level of several pathogenesis-related ( PR ) genes compared to those of MSNs and SiO 2 treatment. By using fluorescein isothiocyanate (FITC)-labeled VMSNs (VMSNs-FITC) to soak the top expanded leaf tips or roots, we observed that the fluorescence of the nanoparticles firstly accumulated at the local site of the top leaves or roots, rapidly migrated to the hypocotyl of rice, and then redistributed sequentially from the bottom leaves to the upper leaves. Furthermore, the foliar or root application of VMSNs-FITC could trigger the local and systemic resistance to PXO99. Notably, no significant toxicity was observed on plants and mice after excessive foliar treatment or feeding tests, respectively. Overall, our research revealed that VMSNs are an effective, systemic, and safe nano-pesticides for controlling rice diseases. Boosting the immune responses may be associated with the transporting of nanoparticles. • Nano silicon can enhance plant resistance to pathogens, with VMSN being the most effective • VMSN can activate rice immunity, enabling rice to acquire systemic acquired resistance • VMSN can improve the resistance of rice to white leaf blight and stripe blight • VMSN can enter the rice body and be transported within the rice body • VMSN has biosafety and is non-toxic to mice

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 score0.021
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.232
Teacher spread0.222 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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