Molecular and biochemical characterization of two odorant-binding proteins from dark black chafer, <i>Holotrichia parallela</i>
Bibliographic record
Abstract
The dark black chafer, Holotrichia parallela Motschulsky, is an economically important pest worldwide. Odorant-based lures and traps are being developed as a key kind of alternative control measures for this pest, and studies to reveal the mechanisms for chemotaxis in this pest are necessary. Two full-length cDNAs encoding different odorant-binding proteins (OBPs) were cloned. The predicted proteins were found to have the functional domains characteristic of typical OBPs and share a high degree of sequence similarity with OBP1 and OBP2 from other insects and were therefore designated as H. parallela OBP-1 and H. parallela OBP-2 (HparOBP-1 and HparOBP-2, respectively). These two OBPs were specifically expressed in antennae. The binding affinity of two purified proteins indicated that HparOBP-1 and HparOBP-2 could selectively interact with various volatiles emitted from host plants and pheromone components. Among the 10 chemicals tested, HparOBP-1 could bind to six of the tested compounds with a dissociation concentration (Ki) less than 20, and HparOBP-2 could bind to three of the compounds. The two OBPs are probably involved in chemotaxis of the dark black chafer. This discovery should accelerate research on chemical communications of this pest, which could potentially lead to the improvement of control measures based on lures and traps.
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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) | 0.001 | 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".