Search

Copyright of Academia: Revista Latinoamericana de Administración is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Academia: Revista Latinoamericana de Administración is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Academia: Revista Latinoamericana de Administración is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Academia: Revista Latinoamericana de Administración is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature. [ABSTRACT FROM AUTHOR]

Dormancy enables plant seeds to time germination until environmental conditions become favorable for seedling survival and growth. The dormancy characteristics of seeds are of important ecological adaptive significance and notable agricultural value. Phytohormone abscisic acid (ABA) and gibberellin (GA) are the key factors for seed dormancy and germination. Mature seeds in dormancy state contain high levels of ABA and low levels of GA. ABA induces and maintains seed dormancy, while GA antagonizes ABA and promotes seed germination. DELAY OF GERMINATION-1 (DOG1) is a major regulator of seed dormancy and had a synergistic effect with ABA to delay germination. DOG1 enhances ABA signal transduction by combining with PP2C ABA hypersensitive germination (AHG1/AHG3), and inhibits the action of AHG1 to increase ABA sensitivity and impose seed dormancy. Imprinted genes are regulated by epigenetic mechanisms before and after fertilization, and are closely related to the establishment and release of seed dormancy. In recent years, remarkable progress has been made in the regulation of seed dormancy. In the present paper, we reviewed the effects of phytohormones ABA and GA on seed dormancy and germination, the action mechanism regulating seed dormancy by DOG1, and the epigenetic regulation of seed dormancy and germination. In addition, we also propose some scientific issues that need to be further investigated in this field to provide some information for understanding the molecular mechanism of seed dormancy and germination, breeding in anti-preharvest sprouting in crop plants, and promoting the germination of dormant seeds. [ABSTRACT FROM AUTHOR]