Using PCR and RCA Techniques to Investigate the Variants of Cassava Mosaic Virus and Their Distribution in Ghana
Bibliographic record
Abstract
Cassava mosaic disease (CMD) caused by cassava begomoviruses is the major constraint to cassava production in Ghana. The disease is known to cause reduction in root yield. To ascertain the distribution of viruses causing CMD, 95 diseased cassava samples were collected in two agroecological zones of Ghana-Deciduous Forest zone and the Transitional zone. On a scale of 1-5, CMD severity was scored. Mean CMD severity score was 2.9, however there was no significant difference (p > 0.05) between the zones. Averagely, CMD score of > 2.8 in 71% of farms visited was recorded. Polymerase chain reaction (PCR) and rolling circle amplification (RCA) were employed for virus identification. PCR revealed that African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV) and mixed infections were prevalent in both zones. EACMV-Cameroon strain was also identified to be common within these zones. The transitional zone had the highest percentage of CMD infection. Unamplified samples from PCR were amplified using rolling circle amplification (RCA) technique. Amplification and characterisation of complete genome sequences of two isolates were carried out. The complete genome of 2780 nucleotides from samples showed a high similarity to African cassava mosaic virus-Ghana (ACMV-GH). Sequences clustered with ACMV-Ivory Coast, ACMV Nigeria-Ogo, ACMV-BF, ACMV-UG having > 96% identity. This shows the close relation that exists amongst the ACMV strains in Africa. These findings highlight the need for a continuous survey of CMD to help manage the disease in the country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".