Differentially expressed immune related genes in bovine neonatal development (VET1P.1114)
Bibliographic record
Abstract
Abstract A thorough understanding of the developing bovine neonatal immune system is important for the development of novel vaccines and therapeutics, as well as to improve breeding practices and farm management, in order to increase the survival and productivity of calves. In this study blood samples were taken from neonates on day of birth before colostrum feed (day 0), and at 7, 14 and 28 days after birth. RNA was extracted from peripheral blood mononuclear cells present in the samples and processed for next generation sequencing. An average of 27 million 151bp reads per sample was generated, and global analysis of the whole transcriptome was performed, based on Hereford cow genome assembly UMD3.1. Differential expression analysis (DE) of immune response related genes were performed at each time point, as well as gene-set enrichment analysis to identify DE genes in immune related pathways comparing day 0 and 7. The analysis identified several immune response related functional coherent DE gene-sets, for example: immune response, defense response, regulation of defense response, cytokine production, and response to cytokine. Within these clusters, the genes with significantly altered expression rates (False Discovery Rate FDR<0.05) and logFC>1 or logFC<-1) were identified. These studies will help to elucidate the mechanisms that are important in bovine neonatal immune development, which will contribute to the development of targeted strategies for disease prevention.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".