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Background: Silicon (Si) is a multiple stress attenuator element in plants, however more research is needed to elucidate the actions in the plants defense system with low nutrition of manganese (Mn) for a prolonged period, and the attenuation mechanisms involved in the effects of Mn deficiency on energy cane with high fiber content. Thus, the objective of this study was to evaluate whether Si reduces the oxidative stress of the energy cane grown in low Mn in nutrient solution, to mitigate the effects of Mn deficiency, improving enzymatic and non-enzymatic defense, uptake of Mn the plant growth. Methods: An experiment was carried out with pre-sprouted seedlings of Saccharum spontaneum L. in a 2 × 2 factorial scheme in five replications in which the plants were grown under sufficiency (20.5 μmol L−1) and deficiency (0.1 μmol L−1) of Mn combined with the absence and presence of Si (2.0 mmol L−1) for 160 days from the application of the treatments. The following parameters were evaluated: accumulation of Mn and Si, H2O2, MDA, activity of SOD and GPOX, total phenol content, pigments, and quantum efficiency of PSII. Results: Mn deficiency induced the oxidative stress for increase the H2O2 and MDA content in leaves of plants and reduce the activity of antioxidant enzymes and total phenols causing damage to quantum efficiency of photosystem II and pigment content. Si attenuated the effects of Mn deficiency even for a longer period of stress by reducing H2O2 (18%) and MDA (32%) content, and increased the Mn uptake efficiency (53%), SOD activity (23%), GPOX (76%), phenol contents, thus improving growth. Conclusions: The supply of Si promoted great nutritional and physiological improvements in energy cane with high fiber content in Mn deficiency. The results of this study propose the supply of Si via fertirrigation as a new sustainable strategy for energy cane cultivation in low Mn environments. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r 2 score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at https://moreiralab.com/resources/poseidon/ , making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake., (© 2024. The Author(s).)