Pronounced Effect of the Nature of the Inoculum on Biofilm Development in Flow Systems
Bibliographic record
Abstract
Biofilm formation renders sessile microbial populations growing in continuous-flow systems less susceptible to variation in dilution rate than planktonic cells, where dilution rates exceeding an organism's maximum growth rate (micro(max)) results in planktonic cell washout. In biofilm-dominated systems, the biofilm's overall micro(max) may therefore be more relevant than the organism's micro(max), where the biofilm micro(max) is considered as a net process dependent on the adsorption rate, growth rate, and removal rate of cells within the biofilm. Together with lag (acclimation) time, the biofilm's overall micro(max) is important wherever biofilm growth is a dominant form, from clinical settings, where the aim is to prevent transition from lag to exponential growth, to industrial bioreactors, where the aim is to shorten the lag and rapidly reach maximum activity. The purpose of this study was to measure CO(2) production as an indicator of biofilm activity to determine the effect of nutrient type and concentration and of the origin of the inoculum on the length of the lag phase, biofilm micro(max), and steady-state metabolic activity of Pseudomonas aeruginosa PA01 (containing gfp), Pseudomonas fluorescens CT07 (containing gfp), and a mixed community. As expected, for different microorganisms the lengths of the lag phase in biofilm development and the biofilm micro(max) values differ, whereas different nutrient concentrations result in differences in the lengths of lag phase and steady-state values but not in biofilm micro(max) rates. The data further showed that inocula from different phenotypic origins give rise to lag time of different lengths and that this influence persists for a number of generations after inoculation.
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 | 0.000 | 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 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".