Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Ultrafine Electrospun Cobalt-Molybdenum Bimetallic Nitride as a Durable Electrocatalyst for Hydrogen Evolution.

Transition metal nitrides are promising electrocatalysts for hydrogen evolution reaction (HER) owing to their Pt-like electronic structure. However, the harsh nitriding conditions greatly limit their large-scale applications. Herein, ultrafine Co3Mo3N-Mo2C (<1 nm)-decorated carbon nanofibers (Co3Mo3N-Mo2C/CNFs) were prepared by electrostatic spinning followed by pyrolysis treatment, in which the MoCo-MOF simultaneously serves as the precursor and nitrogen source. The generated synergistic interactions between Mo2C and Co3Mo3N significantly adjust the electronic structure of Mo2C and afford a fast charge transfer, which endows the resultant hybrid with superior HER electrocatalytic performances. Specifically, the as-obtained Co3Mo3N-Mo2C/CNF delivers a low overpotential of only 76 mV to achieve a current density of 10 mA cm-2 and superior durability with no obvious degradation for 200 h in acidic media. This performance outperforms most of the transition metal-based electrocatalysts reported to date. This work paves a new way for the design of catalysts with ultrasmall size and high efficiency in energy conversion.

Fe-Incorporated Nickel-Based Bimetallic Metal-Organic Frameworks for Enhanced Electrochemical Oxygen Evolution.

Developing cost-effective and high-efficiency catalysts for electrocatalytic oxygen evolution reaction (OER) is crucial for energy conversions. Herein, a series of bimetallic NiFe metal-organic frameworks (NiFe-BDC) were prepared by a simple solvothermal method for alkaline OER. The synergistic effect between Ni and Fe as well as the large specific surface area lead to a high exposure of Ni active sites during the OER. The optimized NiFe-BDC-0.5 exhibits superior OER performances with a small overpotential of 256 mV at a current density of 10 mA cm-2 and a low Tafel slope of 45.4 mV dec-1, which outperforms commercial RuO2 and most of the reported MOF-based catalysts reported in the literature. This work provides a new insight into the design of bimetallic MOFs in the applications of electrolysis.

Dual Electronic Modulations on NiFeV Hydroxide@FeOx Boost Electrochemical Overall Water Splitting.

Nickel-iron based hydroxides have been proven to be excellent oxygen evolution reaction (OER) electrocatalysts, whereas they are inactive toward hydrogen evolution reaction (HER), which severely limits their large-scale applications in electrochemical water splitting. Herein, a heterostructure consisted of NiFeV hydroxide and iron oxide supported on iron foam (NiFeV@FeOx /IF) has been designed as a highly efficient bifunctional (OER and HER) electrocatalyst. The V doping and intimate contact between NiFeV hydroxide and FeOx not only improve the entire electrical conductivity of the catalyst but also afford more high-valence Ni which serves as active sites for OER. Meanwhile, the introduction of V and FeOx reduces the electron density on lattice oxygen, which greatly facilitates desorption of Hads . All of these endow the NiFeV@FeOx /IF with exceptionally low overpotentials of 218 and 105 mV to achieve a current density of 100 mA cm-2 for OER and HER, respectively. More impressively, the electrolyzer requires an ultra-low cell voltage of 1.57 V to achieve 100 mA cm-2 and displays superior electrochemical stability for 180 h, which outperforms commercial RuO2 ||Pt/C and most of the representative catalysts reported to date. This work provides a unique route for developing high-efficiency electrocatalyst for overall water splitting.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network.

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.