Through the degradation of reactive oxygen species (ROS), different antioxidant enzymes, such as catalase (CAT), defend organisms against oxidative stress. These enzymes are crucial to numerous biological functions, like plant development and defense against several biotic and abiotic stresses. However, despite the major economic importance of Avena sativa around the globe, little is known about the CAT gene’s structure and organization in this crop. Thus, a genome-wide investigation of the CAT gene family in oat plants has been carried out to characterize the potential roles of those genes under different stressors. Bioinformatic approaches were used in this study to predict the AvCAT gene’s structure, secondary and tertiary protein structures, physicochemical properties, phylogenetic tree, and expression profiling under diverse developmental and biological conditions. A local Saudi oat variety (AlShinen) was used in this work. Here, ten AvCAT genes that belong to three groups (Groups I–III) were identified. All identified CATs harbor the two conserved domains (pfam00199 and pfam06628), a heme-binding domain, and a catalase activity motif. Moreover, identified AvCAT proteins were located in different compartments in the cell, such as the peroxisome, mitochondrion, and cytoplasm. By analyzing their promoters, different cis-elements were identified as being related to plant development, maturation, and response to different environmental stresses. Gene expression analysis revealed that three different AvCAT genes belonging to three different subgroups showed noticeable modifications in response to various stresses, such as mannitol, salt, and ABA. As far as we know, this is the first report describing the genome-wide analysis of the oat catalase gene family, and these data will help further study the roles of catalase genes during stress responses, leading to crop improvement.

Pneumonia is still a major global health issue, so effective diagnostic methods are needed. This research proposes a new methodology for improving convolutional neural networks (CNNs) and the Visual Geometry Group-16 (VGG16) model by incorporating genetic algorithms (GAs) to detect pneumonia. The work uses a dataset of 5,856 frontal chest radiography images critical in training and testing machine learning algorithms. The issue relates to challenges of medical image classification, the complexity of which can be significantly addressed by properly optimizing CNN. Moreover, our proposed methodology used GAs to determine the hyperparameters for CNNs and VGG16 and fine-tune the architecture to improve the existing performance measures. The evaluation of the optimized models showed some good performances with purely convolutional neural network archetyping, averaging 97% in terms of training accuracy and 94% based on the testing process. At the same time, it has a low error rate of 0.072. Although adding this layer affected the training and testing time, it created a new impression on the test accuracy and training accuracy of the VGG16 model, with 90.90% training accuracy, 90.90%, and a loss of 0.11. Future work will involve contributing more examples so that a richer database of radiographic images is attained, optimizing the GA parameters even more, and pursuing the use of ensemble applications so that the diagnosis capability is heightened. Apart from emphasizing the contribution of GAs in improving the CNN architecture, this study also seeks to contribute to the early detection of pneumonia to minimize the complications faced by patients, especially children.