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
This chapter discusses recent developments in the identification of essential genes and validation of potential antifungal drug targets in Aspergillus fumigatus. Essential genes were identified based on (i) the inability to construct haploid insertional mutants or (ii) identification of temperature-sensitive conditional mutants. The compendium of recently defined conserved essential genes in Saccharomyces cerevisiae, Candida albicans, and other fungi has provided important insights for predicting essential genes in A. fumigatus. Essential genes that are required for fungal survival and growth provide potential antifungal drug targets. A. fumigatus genes which have been experimentally demonstrated to be essential for growth are summarized. This essential gene set includes genes involved in various biological and biochemical functions, such as amino acid, cell wall, ergosterol, heme, and lipid biosynthesis, as well as cell cycle control, cellular metabolism, protein transport, ribosome biogenesis, and RNA splicing. Additional A. fumigatus essential genes involved in ergosterol biosynthesis include ERG10, ERG12, ERG7, ERG8, and ERG20 and as such, provide new targets for therapeutic intervention. Currently, identification of A. fumigatus essential genes largely depends on the following four approaches: conventional gene deletion and disruption, parasexual genetics, RNAi knockdown, and conditional promoter replacement strategies. Completion of the A. fumigatus genome sequence, however, combined with current molecular genetic strategies and their inevitable refinements, has now made large-scale genetic analysis of A. fumigatus possible for the first time, thus expanding our knowledge of its biology, pathogenesis, and potential antifungal targets.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it