Preadipocyte and Adipose Tissue Differentiation in Meat Animals: Influence of Species and Anatomical Location
Bibliographic record
Abstract
Early in porcine adipose tissue development, the stromal-vascular (SV) elements control and dictate the extent of adipogenesis in a depot-dependent manner. The vasculature and collagen matrix differentiate before overt adipocyte differentiation. In the fetal pig, subcutaneous (SQ) layer development is predictive of adipocyte development, as the outer, middle, and inner layers of dorsal SQ adipose tissue develop and maintain layered morphology throughout postnatal growth of SQ adipose tissue. Bovine and ovine fetuses contain brown adipose tissue but SQ white adipose tissue is poorly developed structurally. Fetal adipose tissue differentiation is associated with the precocious expression of several genes encoding secreted factors and key transcription factors like peroxisome proliferator activated receptor (PPAR)γ and CCAAT/-enhancer-binding protein. Identification of adipocyte-associated genes differentially expressed by age, depot, and species in vivo and in vitro has been achieved using single-gene analysis, microarrays, suppressive subtraction hybridization, and next-generation sequencing applications. Gene polymorphisms in PPARγ, cathepsins, and uncoupling protein 3 have been associated with back fat accumulation. Genome scans have mapped several quantitative trait loci (QTL) predictive of adipose tissue-deposition phenotypes in cattle and pigs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".