Specific tracking of xylan using fluorescent-tagged carbohydrate-binding module 15 as molecular probe
Bibliographic record
Abstract
BACKGROUND: Xylan has been identified as a physical barrier which limits cellulose accessibility by covering the outer surface of fibers and interfibrillar space. Therefore, tracking xylan is a prerequisite for understanding and optimizing lignocellulosic biomass processes. RESULTS: In this study, we developed a novel xylan tracking approach using a two-domain probe called OC15 which consists of a fusion of Cellvibrio japonicus carbohydrate-binding domain 15 with the fluorescent protein mOrange2. The new probe specifically binds to xylan with an affinity similar to that of CBM15. The sensitivity of the OC15-xylan detection approach was compared to that of standard methods such as X-ray photoelectron spectroscopy (XPS) and chemical composition analysis (NREL/TP-510-42618). All three approaches were used to analyze the variations of xylan content of kraft pulp fibers. XPS, which allows for surface analysis of fibers, did not clearly indicate changes in xylan content. Chemical composition analysis responded to the changes in xylan content, but did not give any specific information related to the fibers surface. Interestingly, only the OC15 probe enabled the highly sensitive detection of xylan variations at the surface of kraft pulp fibers. At variance with the other methods, the OC15 probe can be used in a high throughput format. CONCLUSIONS: We developed a rapid and high throughput approach for the detection of changes in xylan exposure at the surface of paper fibers. The introduction of this method into the lignocellulosic biomass-based industries should revolutionize the understanding and optimization of most wood biomass processes.
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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 | 0.000 | 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) | 0.001 | 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 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".