Effects on Cell Growth, Lipid and Biochemical Composition of Thalassiosira weissflogii (Bacillariophyceae) Cultured under Two Nitrogen Sources
Bibliographic record
Abstract
The protein and polyunsaturated fatty acid (PUFA) enrichment of microalgae can improve their nutritional value for larvae of various reared organisms. Protein enrichment can be achieved by increasing nitrogen concentration and selecting nitrogen sources that are easy to assimilate during microalga culture. Nitrogen-rich cultures can increase organism growth, biomass, and protein content, but their lipid content tends to stall. Since the diatom Thalassiosira weissflogii is usually provided to feed shrimp larvae, this study evaluated its digestibility and biochemical composition, culturing it with two nitrogen sources (NaNO3 and NH4Cl) at different concentrations (111.25, 222.50, 445 and 890 µM). The cell abundance, dry-weight biomass, Chl a, proteins, carbohydrates, total lipids and essential fatty acids were determined. The cell density and biomass peaked faster (day 12) with treatment < 890 µM than with 890 µM (day 15) in both nitrogen sources. However, the highest cell density, biomass and peak protein yield were not significantly different among treatments, suggesting the need to compare maintenance costs for a given production. After nine days of culture, concentrations ≤ 222.5 µM increased lipid content irrespective of the nitrogen source and decreased by 10–20% afterwards. With higher lipid production, the dominant PUFA were eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). One gram of NH4Cl provides ~60% more nitrogen than 1 g of NaNO3. In conclusion, based on time and growth rate, T. weissflogii cultivated with NH4Cl at 222.50 µM produced EPA and DHA at a better yield–cost ratio for biomass and lipid production. Furthermore, its nutritional value as enriched live-food for the reared larvae of marine organisms suggests potential biotechnological applications for aquaculture.
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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) | 0.000 | 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".