Focus on the Plant Endomembrane System in Molecular Plant-Microbe Interactions
Bibliographic record
Abstract
Interactions between plants and microbes are ubiquitous. The outcomes of these interactions involve interkingdom communication, with myriad, diverse signals moving between microbes and their potential plant hosts. Years of biochemical, genetic, and molecular biology research have provided an overview of the landscape of the repertoires of effectors and elicitors encoded by microbes that allow them to stimulate and manipulate responses from their potential plant hosts. Similarly, considerable insight into the plant machinery and capacity for responding to microbes has been gained. The advent of new bioinformatics and modeling approaches has greatly contributed to our understanding of how these interactions occur, and it is expected that these tools, coupled with burgeoning genome sequencing data, will eventually allow the prediction of the outcome of these interactions and whether they will result in a relationship that benefits one or both partners. As a complement to these studies, cell biological studies are elucidating how cells in the plant hosts behave in response to microbial signals. Such studies have brought new attention to the indispensable role of the plant endomembrane system in determining the outcome of plant-microbe interactions. This Focus Issue addresses not only how the plant endomembrane acts locally to mediate responses to microbes but, also, the importance of the plant endomembrane beyond the plant cell borders for cross-kingdom effects. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2023.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".