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The exploration of highly efficient noble-metal-free catalysts for overall water splitting is crucial for industrial application. In this study, FeS nanosheets/1T-MoS2 nanoflowers on conductive iron foam (IF) were prepared as efficient electrocatalysts for the HER and OER using a solvothermal method. The prepared FMS0.5 electrocatalyst can deliver a high current density of 100 mA cm−2 at low overpotential potentials of 245 and 316 mV and small Tafel slopes of 80.6 and 88.3 mV dec−1 for the HER and OER. The FMS0.5//FMS0.5 exhibited a low voltage of 1.56 V at 10 mA cm−2 with remarkable stability for 40 h in alkaline water. Benefiting from the 3D morphology and interface coupling interactions between the FeS nanosheets and 1T-MoS2 nanoflowers, the self-supporting electrocatalyst could tune the charge/ion transfer path and electronic structure to facilitate the HER and OER activity. Hence, this study would provide new ideas for exploiting highly active non-noble-metal bifunctional catalysts for energy-related applications. [ABSTRACT FROM AUTHOR]

Current methods of molecular image-based drug discovery face two major challenges: (1) work effectively in absence of labels, and (2) capture chemical structure from implicitly encoded images. Given that chemical structures are explicitly encoded by molecular graphs (such as nitrogen, benzene rings and double bonds), we leverage self-supervised contrastive learning to transfer chemical knowledge from graphs to images. Specifically, we propose a novel Contrastive Graph-Image Pre-training (CGIP) framework for molecular representation learning, which learns explicit information in graphs and implicit information in images from large-scale unlabeled molecules via carefully designed intra- and inter-modal contrastive learning. We evaluate the performance of CGIP on multiple experimental settings (molecular property prediction, cross-modal retrieval and distribution similarity), and the results show that CGIP can achieve state-of-the-art performance on all 12 benchmark datasets and demonstrate that CGIP transfers chemical knowledge in graphs to molecular images, enabling image encoder to perceive chemical structures in images. We hope this simple and effective framework will inspire people to think about the value of image for molecular representation learning. [ABSTRACT FROM AUTHOR]

Hollow hetero-nanosheet arrays have attracted great attention due to their efficient catalytic abilities for water splitting. We successfully fabricated ZIF-67-derived hollow CoMoS3.13/MoS2 nanosheet arrays on carbon cloth in situ through a two-step heating-up hydrothermal method, in which the MoS2 nanosheets were suitably distributed on the surface of the hollow CoMoS3.13 nanosheet arrays. There was a distinct synergistic effect between CoMoS3.13 and MoS2, and a large number of defective and disordered interfaces were formed, which improved the charge transfer rate and provided abundant electrochemical active sites. CMM 0.5, with the optimal Mo doping concentration of 0.5 mmol, exhibited the best catalytic properties. The overpotential values of CMM 0.5 at 10 mA cm−2 were only 107 and 169 mV for the HER and OER, respectively, and it had nearly 100% faradaic efficiency. A dual-electrode electrolytic cell assembled with CMM 0.5 required a voltage of only 1.507 V at 10 mA cm−2 for overall water splitting, and it displayed remarkable long-term durable bifunctional stability. [ABSTRACT FROM AUTHOR]

The interaction between microribonucleic acid and long non-coding ribonucleic acid plays a very important role in biological processes, and the prediction of the one is of great significance to the study of its mechanism of action. Due to the limitations of traditional biological experiment methods, more and more computational methods are applied to this field. However, the existing methods often have problems, such as inadequate acquisition of potential features of the sequence due to simple coding and the need to manually extract features as input. We propose a deep learning model, preMLI, based on rna2vec pre-training and deep feature mining mechanism. We use rna2vec to train the ribonucleic acid (RNA) dataset and to obtain the RNA word vector representation and then mine the RNA sequence features separately and finally concatenate the two feature vectors as the input of the prediction task. The preMLI performs better than existing methods on benchmark datasets and has cross-species prediction capabilities. Experiments show that both pre-training and deep feature mining mechanisms have a positive impact on the prediction performance of the model. To be more specific, pre-training can provide more accurate word vector representations. The deep feature mining mechanism also improves the prediction performance of the model. Meanwhile, The preMLI only needs RNA sequence as the input of the model and has better cross-species prediction performance than the most advanced prediction models, which have reference value for related research. [ABSTRACT FROM AUTHOR]

The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases and the search for therapeutic drugs. However, the increase in data also increases the difficulty of biological networks analysis. Therefore, algorithms that can handle large, heterogeneous and complex data are needed to better analyze the data of these network structures and mine their useful information. Deep learning is a branch of machine learning that extracts more abstract features from a larger set of training data. Through the establishment of an artificial neural network with a network hierarchy structure, deep learning can extract and screen the input information layer by layer and has representation learning ability. The improved deep learning algorithm can be used to process complex and heterogeneous graph data structures and is increasingly being applied to the mining of network data information. In this paper, we first introduce the used network data deep learning models. After words, we summarize the application of deep learning on biological networks. Finally, we discuss the future development prospects of this field. [ABSTRACT FROM AUTHOR]

Hollow hetero-nanosheet arrays have attracted great attention due to their efficient catalytic abilities for water splitting. We successfully fabricated ZIF-67-derived hollow CoMoS 3.13 /MoS 2 nanosheet arrays on carbon cloth in situ through a two-step heating-up hydrothermal method, in which the MoS 2 nanosheets were suitably distributed on the surface of the hollow CoMoS 3.13 nanosheet arrays. There was a distinct synergistic effect between CoMoS 3.13 and MoS 2 , and a large number of defective and disordered interfaces were formed, which improved the charge transfer rate and provided abundant electrochemical active sites. CMM 0.5, with the optimal Mo doping concentration of 0.5 mmol, exhibited the best catalytic properties. The overpotential values of CMM 0.5 at 10 mA cm -2 were only 107 and 169 mV for the HER and OER, respectively, and it had nearly 100% faradaic efficiency. A dual-electrode electrolytic cell assembled with CMM 0.5 required a voltage of only 1.507 V at 10 mA cm -2 for overall water splitting, and it displayed remarkable long-term durable bifunctional stability.