Bibliographic record
Abstract
In nature,the biofilm mode of life is of great importance in the cell cycle for many microorganisms and is one of hotspots in microbiology. However,the progress of studies of biofilm due to the complexity and variability of associated physiological activities requires advanced methods with high throughput. In this paper,principles,applications,advantages and disadvantages of several biofilm growing devices and appropriate methods for biofilm analysis are discussed,together with several potential methods for the control and removal of biofilms. Among the biofilm growing devices,the microtiter-plate is the first that has been used in high-throughput testing of biofilms. But it is not possible to assess the biofilm structure only by microscopy. The Calgary Biofilm Device was applied for that purpose. Another high-throughput biofilm forming device is the BioFilm Ring. For the analysis of the structure of the biofilms,the microscopy plays a key role. Coupled with microscopes,some fluorescent dyes such as DAPI,CTC and SYTO 9/PI are used for the assessment of total cells,physiologically-active cells and membrane compromised cells,respectively. For the analysis of the chemical substances of the biofilms,microprobes are usually used for the detection of DO and pH. EPS and Quorum sensing signal molecules are usually measured by extraction methods. Some new methods have been suggested that may be developed to release bacteria from the surfaces,for instance,antifoulants,hydrolases and NO. These would be alternatives to biocides in the future. Recent advances in high-throughput methods to generate and study biofilms have opened the possibility of starting an omics-biofomics approach to study these complex structures. The biofomics will reach the application stage in areas such as clinical,industrial and environmental microbiology in the next decades.
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 | 0.001 | 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".