Extracts From Leaves of Allamanda blanchetti Inducing Mechanism of Defense to Diseases in Sugarcane
Bibliographic record
Abstract
This research aims to analyze the effect of the extract from a native plant from Caatinga, Allamanda blanchetti, in the induction of resistance mechanisms in a sugarcane variety smut-susceptible. Initially, it carried out a phytochemical analysis to know the main plant compounds used in this study. Diverse chemicals content in ethanolic extract from A. blanchetii were detected by thin-layer chromatography (TLC). Flavonoids, were more abundant compounds following by terpenes, stereroids and saponins. Under greenhouse conditions the sugarcane plants, SP-791011 (smut-susceptible), were sprayed with extracts from A. blanchetti extracted at cold at concentrations of 1000 ppm and acilbenzolar-S-metil (ASM) (100 mg/L). Leaves were collected at 0, 24 and 48 hours after spraying and used in the RT-PCR analysis for to identify the defense gene expression. Change in gene expression were observed in the different treatments, especially in the expression of pathogenesis-related (PR) genes. The extract of A. blanchetti induced an increase in the glucanase expression and was more effective than ASM inducer. SNPR1 gene show increased in the two treatment. The results indicate that A. blanchetti extracts was able to activate the resistance mechanism as observed in resistant plants. This paper is the first report about the use of Caatinga natural plant extracts inducing resistance genes against Sporisorium scitamineum in sugarcane susceptible genotype.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".