Molybdenum (Mo) is considered and described as an essential element for living organisms’ development. Until now, no studies have been performed on genes involved in the Mo transporter in ancestral Ipomoea species. This study aimed to identify potential Mo genes in Ipomoea trifida and I. triloba genomes using bioinformatics tools. We identified four Mo transporter genes, two in I. trifida and two in I. triloba. Based on the RNA-seq datasets, we observed that Mo genes are expressed (in silico) and present different mechanisms between the tissues analyzed. The information generated in this study fills missing gaps in the literature on the Mo gene in an important agronomic crop.

In higher eukaryotes, the genes’ architecture has become an essential determinant of the variation in the number of transcripts (expression level) and the specificity of gene expression in plant tissue under stress conditions. The modern rise in genome-wide analysis accounts for summarizing the essential factors through the translocation of gene networks in a regulatory manner. Stress tolerance genes are in two groups: structural genes, which code for proteins and enzymes that directly protect cells from stress (such as genes for transporters, osmo-protectants, detoxifying enzymes, etc.), and the genes expressed in regulation and signal transduction (such as transcriptional factors (TFs) and protein kinases). The genetic regulation and protein activity arising from plants’ interaction with minerals and abiotic and biotic stresses utilize high-efficiency molecular profiling. Collecting gene expression data concerning gene regulation in plants towards focus predicts an acceptable model for efficient genomic tools. Thus, this review brings insights into modifying the expression study, providing a valuable source for assisting the involvement of genes in plant growth and metabolism-generating gene databases. The manuscript significantly contributes to understanding gene expression and regulation in plants, particularly under stress conditions. Its insights into stress tolerance mechanisms have substantial implications for crop improvement, making it highly relevant and valuable to the field.

António Tapiés is considered a master artist in Spain with the same influence as Picasso, Miró and Dali. Throughout his artistic career, Antoni Tapiés has formed distinctive and personal artistic expression characteristics and forms of expression in his continuous development and changes. The development and aesthetic habits of contemporary art have also been influenced by his work. When we combine his creative background and learning process, we can more accurately analyze his mysterious artistic characteristics and unique forms of expression. When we combine the background and study process of Antoni Tapiés, we can more accurately analyze his mysterious artistic expressions and unique forms of expression.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.