Additional file 1 of Strain engineering and bioprocessing strategies for biobased production of porphobilinogen in Escherichia coli
Bibliographic record
Abstract
Additional file 1: Table S1. DNA oligonucleotide sequences used in this study. Table S2. gRNA sequences targeting hemC for CRISPRi in this study. See Additiona file 1: Figure S1 for qRT-PCR results for select gRNAs. Table S3. Tabulated images of bioreactor cultivation samples under aerobic and microaerobic conditions. Table S4. Statistical analysis for comparing experimental data of PBG titers. Figure S1. Quantification of the relative hemC expression for select gRNAs using qRT‐PCR. All qRT‐PCR values are reported as means ± SD (n = 2). Figure S2. Bioreactor cultivation of DSL-D1∆iclR∆sdhA, DSL-D2∆iclR∆sdhA, DSL-D3∆iclR∆sdhA, and DSL-D4∆iclR∆sdhA for PBG biosynthesis under aerobic conditions. Time profiles of cell density (OD600), glycerol consumption and metabolite extracellular accumulation profiles are shown. (I) DSL-D1∆iclR∆sdhA, (II) DSL-D2∆iclR∆sdhA, (III) DSL-D3∆iclR∆sdhA, (IV) DSL-D4∆iclR∆sdhA. All values are reported as means ± SD (n = 2). Figure S3. Bioreactor cultivation of DSL-D5∆iclR∆sdhA, DSL-D6∆iclR∆sdhA, DSL-D7∆iclR∆sdhA, and DSL-D8∆iclR∆sdhA for PBG biosynthesis under aerobic conditions. Time profiles of cell density (OD600), glycerol consumption and metabolite extracellular accumulation profiles are shown. (I) DSL-D5∆iclR∆sdhA, (II) DSL-D6∆iclR∆sdhA, (III) DSL-D7∆iclR∆sdhA, (IV) DSL-D8∆iclR∆sdhA. All values are reported as means ± SD (n = 2).
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.803 | 0.169 |
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".