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Small RNAs are key regulators in plant growth and development. One subclass, phased siRNAs (phasiRNAs) require a trigger microRNA for their biogenesis. In grasses, two pathways yield abundant phasiRNAs during anther development; miR2275 triggers one class, 24-nt phasiRNAs, coincident with meiosis, while a second class of 21-nt phasiRNAs are present in premeiotic anthers. Here we report that the 24-nt phasiRNA pathway is widely present in flowering plants, indicating that 24-nt reproductive phasiRNAs likely originated with the evolutionary emergence of anthers. Deep comparative genomic analyses demonstrated that this miR2275/24-nt phasiRNA pathway is widely present in eudicots plants, however, it is absent in legumes and in the model plant Arabidopsis, demonstrating a dynamic evolutionary history of this pathway. In Solanaceae species, 24-nt phasiRNAs were observed, but the miR2275 trigger is missing and some loci displaying 12-nt phasing. Both the miR2275-triggered and Solanaceae 24-nt phasiRNAs are enriched in meiotic stages, implicating these phasiRNAs in anther and/or pollen development, a spatiotemporal pattern consistent in all angiosperm lineages that deploy them.

As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal's marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN- Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. "ACO-TCN-Attention" is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy. [ABSTRACT FROM AUTHOR]