Enhancing Postharvest Characteristics in Durian via Genome Editing: Regulation of Pericarp Softening and Shelf-Life Extension
Bibliographic record
Abstract
This study explored the use of CRISPR-mediated gene editing to reduce the softening rate of durian peel and delay senescence, focusing on key targets such as ethylene synthesis pathways (such as ACS2 and ACO1), cell wall degradation enzymes (such as PG, PME, EXP), peel cuticle formation (CER1) and antioxidant pathways (such as SOD, CAT, AOX). Strategies for applying genome editing to durian improvement were proposed, including phenotypic screening of edited lines and mechanistic analysis of how editing regulates the softening process at the molecular level. The potential advantages of breeding harder and more storable durian varieties were also discussed, including reducing postharvest losses, expanding market channels and improving economic benefits. At the same time, the challenges and risks that may be faced in this process were analyzed, such as off-target effects, regulatory barriers and the need to maintain the flavor quality of the fruit. Combined with the molecular biology research results of durian and the successful experience of gene editing in other fruit trees, genome editing technology is expected to become an important tool for improving the postharvest characteristics of durian, which can change the supply chain of durian, extend shelf life and maintain quality, thus benefiting growers, distributors and consumers.
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".