Impact of low‐intensity pulsed ultrasound on the growth of <i>Schizochytrium sp</i>. for omega‐3 production
Bibliographic record
Abstract
Abstract Schizochytrium sp. is a microalga that is known for its high content of oils or lipids. It has a high percentage of polyunsaturated fatty acids in the accumulated oil, especially docosahexaenoic acid (DHA). DHA is an important additive for the human diet. Large‐scale production of Schizochytrium sp. can serve as an alternative source of DHA for humans as well as for fish feed, decreasing the burden on aqua systems. Therefore, research on improving the productivity of Schizochytrium attracts a lot of attention. We studied the potential of using low‐intensity pulsed ultrasound (LIPUS) in the growth cycle of Schizochytrium sp. in shake flasks. Different intensities and treatment durations were tested. A positive effect of LIPUS on biomass accumulation was observed in the Schizochytrium sp. culture. Specifically, LIPUS stimulation at the ultrasound intensity of 400 mW/cm2 with 20 min per treatment 10 times a day with equal intervals of 2.4 h between the treatments was found to enhance the growth of Schizochytrium biomass most effectively (by up to 20%). Due to the nature of cell division in Schizochytrium sp. which occurs via zoospore formation, LIPUS stimulation was inefficient if applied continuously during all 5 days of the growth cycle. Using microscopy, we studied the interval between zoospore formation in the culture and selected the optimal LIPUS application days (Days 0–1 and Days 4–5 of the 5‐day growth cycle). Microscopic images have also shown that LIPUS stimulation enhances zoospore formation in Schizochytrium sp., leading to more active cell division in the culture. This study shows that LIPUS can serve as an additional tool for cost‐efficiency improvement in the large‐scale production of Schizochytrium as a sustainable and environmentally friendly source of omega‐3 (DHA).
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".