RESUMO
Background: Soil salinity negatively impacts agricultural productivity. Consequently, strategies should be developed to inculcate a salinity tolerance in crops for sustainable food production. Growth regulators play a vital role in regulating salinity stress tolerance. Methods: Thus, we examined the effect of exogenous salicylic acid (SA) and alpha-tocopherol (TP) (100 mg/L) on the morphophysio-biochemical responses of two wheat cultivars (Pirsabak-15 and Shankar) to salinity stress (0 and 40 mM). Results: Both Pirsabak-15 and Shankar cultivars were negatively affected by salinity stress. For instance, salinity reduced growth attributes (i.e., leaf fresh and dry weight, leaf moisture content, leaf area ratio, shoot and root dry weight, shoot and root length, as well as root-shoot ratio), pigments (chlorophyll a, chlorophyll a, and carotenoids) but increased hydrogen peroxide (H2O2), malondialdehyde (MDA), and endogenous TP in both cultivars. Among the antioxidant enzymes, salinity enhanced the activity of peroxidase (POD) and polyphenol oxidase (PPO) in Pirsabak-15; glutathione reductase (GR) and PPO in Shankar, while ascorbate peroxidase (APOX) was present in both cultivars. SA and TP could improve the salinity tolerance by improving growth and photosynthetic pigments and reducing MDA and H2O2. In general, the exogenous application did not have a positive effect on antioxidant enzymes; however, it increased PPO in Pirsabak-15 and SOD in the Shankar cultivar. Conclusions: Consequently, we suggest that SA and TP could have enhanced the salinity tolerance of our selected wheat cultivars by modulating their physiological mechanisms in a manner that resulted in improved growth. Future molecular studies can contribute to a better understanding of the mechanisms by which SA and TP regulate the selected wheat cultivars underlying salinity tolerance mechanisms.
RESUMO
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.