Improvement of track zero to increase read/write area in hard disk drive assembly process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.
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.
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 | 0.000 | 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) | 0.000 | 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 it