Freeze‐substitution and low‐temperature embedding of dairy products for transmission electron microscopy
Bibliographic record
Abstract
Dairy products are comprised largely of fat, air and water, which makes it difficult to preserve their ultrastructure for electron microscopy. Keeping the samples frozen throughout fixation and embedding protects the structure and distribution of the components of emulsions and foams. Therefore, dairy products were freeze-substituted and embedded at low temperature (-20 degrees C) to prepare them for transmission electron microscopy. Whipped cream, ice cream mix and dairy/non-dairy mixed systems were frozen by plunging in propane, at its boiling point (-187 degrees C). Ice cream, because it is already frozen, was fractured into 1-mm3 pieces in liquid nitrogen and then added to frozen fixative (-196 degrees C). Fixative solution consisted of glutaraldehyde, osmium tetroxide and uranyl acetate dissolved in either methanol or acetone. When material was to be stained after sectioning the fixative was limited to glutaraldehyde in methanol. The temperature was increased step-wise from -80 to -20 degrees C. Solvent was replaced with resin; the polar resin Lowicryl HM4, the non-polar resin Lowicryl HM20, LR White and LR Gold were tested. Samples were embedded and polymerized at -20 degrees C using ultraviolet light to cross-link the resin. Methanol proved to be the most effective solvent for substituting the ice; the hydrophobic resin Lowicryl HM20 was the most effective resin for retaining fat structure following osmium fixation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".