Seasonal variation of microbiota composition in <i>Anopheles gambiae</i> and <i>Anopheles coluzzii</i> in two different eco‐geographical localities in Cameroon
Bibliographic record
Abstract
Understanding the environmental factors affecting the microbiota in malaria vectors may help in the development of novel vector control interventions, similar to paratransgenesis. This study evaluated seasonal and geographical variations in the microbial community of the two major malaria vectors. Adult Anopheles mosquitoes were collected across two different eco-geographical settings in Cameroon, during the dry and wet seasons. DNA was extracted from the whole individual mosquitoes from each group and processed for microbial analysis using Illumina Miseq sequencing of the V3-V4 region of the 16S rRNA gene. Data analysis was performed using QIIME2 and R software programs. A total of 1985 mosquitoes were collected and among them, 120 were selected randomly corresponding to 30 mosquitoes per season and locality. Overall, 97 bacterial taxa were detected across all mosquito samples, with 86 of these shared between dry and wet seasons in both localities and species. There were significant differences in bacterial composition between both seasons, with a clear separation observed between the dry and wet seasons (PERMANOVA comparisons of beta diversity, Pseudo-F = 10.45; q-value = 0.01). This study highlights the influence of seasonal variation on microbial communities and this variation's impact on mosquito biology and vectorial capacity should be further investigated.
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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 | 0.001 | 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".