Seaweed Extract (Stella Maris®) Activates Innate Immune Responses in Arabidopsis thaliana and Protects Host against Bacterial Pathogens
Bibliographic record
Abstract
Insects and pathogenic infections (bacteria, viruses and fungi) cause huge losses in agriculturally important crops yearly. Due to the rise in pesticide and antibiotic resistance, our crops and livestock are increasingly at risk. There is a rising demand for environmentally friendly solutions to prevent crop decreases. Components of Ascophyllum nodosum seaweed extracts were recently found to boost plant immunity. The stimulatory activities of the A.nodosum marine alga-derived extract (Stella Maris®) were investigated in a broad range of immune assays. Elevated hydrogen peroxide production measured in a chemiluminescence assay suggested that the extract elicited a strong burst of reactive oxygen species. Arabidopsis seedlings treated with Stella Maris® activated the expression of WRKY30, CYP71A12 and PR-1 genes, the induction of which represent early, mid and late plant immune response, respectively. Finally, this study found that Stella Maris® inhibited the growth of multiple bacterial pathogens, including an opportunistic human pathogen that has demonstrated pathogenicity in plants. In summary, the pre-treatment with the seaweed extract protected Arabidopsis against subsequent infection by these pathogens.
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) | 0.000 | 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 | 0.000 |
| 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".