Molecular markers and antioxidant activity in berry crops: Genetic diversity analysis
Bibliographic record
Abstract
Debnath, S. C., Siow, Y. L., Petkau, J., An, D. and Bykova, N. V. 2012. Molecular markers and antioxidant activity in berry crops: Genetic diversity analysis. Can. J. Plant Sci. 92: 1121-1133. An improved understanding of important roles of dietary fruits in maintaining human health has led to a dramatic increase of global berry crop production. Berry fruits contain relatively high levels of vitamin C, cellulose and pectin, and produce anthocyanins, which have important therapeutic values, including antitumor, antiulcer, antioxidant and anti-inflammatory activities. There is a need to develop reliable methods to identify berry germplasm and assess genetic diversity/relatedness for dietary properties in berry genotypes for practical breeding purposes through genotype selection in a breeding program for cultivar development, and proprietary-rights protection. The introduction of molecular biology techniques, such as DNA-based markers, allows direct comparison of different genetic materials independent of environmental influences. Significant progress has been made in diversity analysis of wild cranberry, lowbush blueberry, lingonberry and cloudberry germplasm, and in strawberry and raspberry cultivars and advanced breeding lines developed in Canada. Inter simple sequence repeat (ISSR) markers detected an adequate degree of polymorphism to differentiate among berry genotypes, making this technology valuable for cultivar identification and for the more efficient choice of parents in the current berry improvement programs. Although multiple factors affect antioxidant activity, a wide range of genetic diversity has been reported in wild and cultivated berry crops. Diversity analysis based on molecular markers did not agree with those from antioxidant activity. The paper also discusses the issues that still need to be addressed to utilize the full potential of molecular techniques including expressed sequence tag-polymerase chain reaction (EST-PCR) analysis to develop improved environment-friendly berry cultivars suited to the changing needs of growers and consumers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".