Background
It is crucial to gain understanding of crop diversity at the genetic and phenotypic levels. For breeding onion with higher yield and quality together with greater resilience to biotic and abiotic stressors agro-morphological and molecular characterization of onion genotypes is of utmost importance.
Methods and results
In the present study, genetic diversity in 49 onion genotypes were assessed using 6 agro-morphological descriptors, 19 quantitative traits along with 23 ISSR markers. All the agro-morphological descriptors were found polymorphic with Bulb: basic colour of dry skin (1.44) exhibiting the highest diversity index. The multivariate analysis using Mahalanobis D2 statistic grouped 49 genotypes into seven clusters with highest inter cluster distance between V and VII (364.35). A total of 78 fragments were produced from 13 polymorphic primers with a mean of 6 alleles per primer. The polymorphic information content (PIC) ranged from 0.42 (UBC 835) to 0.75 (UBC 825) with a mean of 0.61 per primer. With a mean of 0.36, the inter-genotype genetic distance ranged from 0.12 to 0.72. Based on cluster analysis using UPGMA algorithm, the genotypes were divided into two major clusters, whereas the cluster tree constructed using the described ISSR markers identified three major groups. The structure analysis divided the population into two main groups.
Conclusion
From the findings of present study, it can be stated that characterization at both molecular and morphological basis is of utmost importance to understand the genetic diversity in onion. Hybridization between distantly related genotypes can produce desirable transgressive segregants in future onion breeding programmes.
Background:
Cancer is a deadly disease. It is crucial to diagnose cancer in its early stages. This can be done with medical imaging. Medical imaging helps us scan and view internal organs. The analysis of these images is a very important task in the identification and classification of cancer. Over the past years, the occurrence of cancer has been increasing, so it has been a load on the medical fraternity. Fortunately, with the growth of Artificial Intelligence in the past decade, many tools and techniques have emerged which may help doctors in the analysis of medical images.
Methodology:
This is a systematic study covering various tools and techniques used for medical image analysis in the field of cancer detection. It focuses on machine learning and deep learning technologies, their performances, and their shortcomings. Also, the various types of imaging techniques and the different datasets used have been discussed extensively. This work also discusses the various pre-processing techniques that have been performed on medical images for better classification.
Results:
A total of 270 studies from 5 different publications and 5 different conferences have been included and compared on the above-cited parameters.
Conclusion:
Recommendations for future work have been given towards the end.
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