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Ammonia (NH3) is recognized as a transportable carrier for renewable energy fuels. Photoelectrochemical nitrate reduction reaction (PEC NO3RR) offers a sustainable solution for nitrate‐rich wastewater treatment by directly converting solar energy to ammonia. In this study, we demonstrate the highly selective PEC ammonia production from NO3RR by constructing a CoCu/TiO2/Sb2Se3 photocathode. The constructed CoCu/TiO2/Sb2Se3 photocathode achieves an ammonia Faraday efficiency (FE) of 88.01 % at −0.2 VRHE and an ammonia yield as high as 15.91 μmol h−1 cm−2 at −0.3 VRHE with an excellent onset potential of 0.43 VRHE. Dynamics experiments and theoretical calculations have demonstrated that the CoCu/TiO2/Sb2Se3 photocathode possesses high light absorption capacity, excellent carrier transfer capability, and high charge separation and transfer efficiencies. The photocathode can effectively adsorb the reactant NO3− and intermediate, and the CoCu co‐catalyst increases the maximum Gibbs free energy difference between NO3RR and HER. Meanwhile, the Co species enhances the spin density of Cu, and increases the density of states near the Fermi level in pdos, which results in a high PEC NO3RR activity on CoCu/TiO2/Sb2Se3. This work provides a new avenue for the feasibility of efficient PEC ammonia synthesis from nitrate‐rich wastewater. [ABSTRACT FROM AUTHOR]

Objective/Background: The genus Diospyros L. (Ebenaceae) includes about 778 species worldwide, with over 500 of them known for their significant health benefits. Among them, Diospyros mollis Griff. has been used as a tonic and treatment for many diseases related to digestive disorders and intestinal parasites. Furthermore, the fruit has also been used for dyeing silk fabrics. The purpose of this comprehensive review is to provide researchers with important information about D. mollis, particularly regarding its chemical composition and biological effects. This information can support current understanding and future studies to develop novel herbal formulations based on this species. Additionally, this review aims to compile and summarize the available literature on this species, covering its botanical, ethnomedicinal, phytochemical, and pharmacological properties reported to date. Methods: The relevant information on D. mollis was collected via online electronic and databases, including Scopus, Pubmed, Web of Science, Google Scholar, ScienceDirect, Springer Link, SciFinder, and other scientific databases. Results: A comprehensive review of the literature reveals that D. mollis contains various chemical constituents such as naphthalene and naphthoquinone derivatives, triterpenoids, alkaloids, sterols, glycosides, and fatty acid esters isolated from the berry, root, bark, and leaf of this species, with several pharmacological effects and other applications. Many pharmacological effects, such as hyaluronidase inhibitory, anthelmintic, and antiparasitic activities of various extracts and isolated compounds of D. mollis, are reported in this review. The review demonstrates the importance of D. mollis in the treatment of diseases, its application in fabric dyeing techniques, and its traditional use. Conclusion: In conclusion, D. mollis emerges as a promising botanical resource with its rich history in traditional medicine and diverse chemical constituents, paving the way for potential future applications and innovations in the field of herbal medicine and drug development. [ABSTRACT FROM AUTHOR]

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors. [ABSTRACT FROM AUTHOR]

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation. [ABSTRACT FROM AUTHOR]

Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems. [ABSTRACT FROM AUTHOR]