Applications of liquid extracts from the brown algae Ascophyllum nodosum (Fucales) and Laminaria digitata (Laminariales) to enhance the growth of eucheumatoids in tissue culture
Bibliographic record
Abstract
Abstract Eucheuma denticulatum and Kappaphycus alvarezii, two tropical red seaweeds, are two of the most commercially important, cultivated carrageenophytes in Southeast Asia. Their large-scale farming through repeated vegetative propagation and virtual mono-cropping has resulted in a variety of phyconomic issues. The efficacy of two European, commercial liquid extracts from the brown seaweeds, i.e., Ascophyllum nodosum (ANE), a fucoid, and Laminaria digitata (LDE), a kelp, on the direct formation of axes and survival of these two eucheumatoids was examined using tissue culture techniques. Seaweed apical sections (3–5 mm long) were cultured for 45 days, with different concentrations of ANE and LDE (i.e., 0.005, 0.05, 0.5, 1.5, and 2.5 mL L− 1) and compared with a control (UV-filtered seawater). Both liquid extracts improved the growth and morphogenesis of E. denticulatum and K. alvarezii in tissue culture. This study recommends a dilution rate of 0.05 mL LDE per L for E. denticulatum tissue culture resulting in a high percentage survival (i.e., 95.3 ± 0.9%), formation of direct axes (i.e., 95.3 ± 0.9%), and the longest length (i.e., 10.0 ± 0.3 mm) after a typical 45-day culture period. These data combined with the highest percentage formation of axes on day 14 (i.e., 88.0 ± 4.9%). However, 0.5 mL ANE per L is recommended for the tissue culture of K. alvarezii, given the relatively high final survival and direct axis formation (i.e., 96.2 ± 2.2%) and shoot length (i.e., 8.2 ± 0.1 mm). This study supports the application of these temperate, brown seaweed-derived extracts as phycobiostimulant enrichment in eucheumatoid micropropagation for mass production of plantlets for out-planting purposes.
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 | 0.000 |
| 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".