A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations.

BACKGROUND: In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases. RESULTS: In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well. CONCLUSIONS: Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

Three new Pyrenula species with 3-septate ascospores with red or orange oil when over-mature (Ascomycota, Pyrenulales, Pyrenulaceae) from China.

The lichenised fungal genus Pyrenula is a very common crustose lichen element in tropical to subtropical forests, but little research has been done on this genus in China. During our study on Pyrenula in China, based on morphological characteristics, chemical traits and molecular phylogenetic analysis (ITS and nuLSU), three new 3-septate species with red or orange oil in over-mature ascospores were found: Pyrenulainspersasp. nov., P.thailandicoidessp. nov. and P.apiculatasp. nov. Compared to the known 3-septate species of Pyrenula with red or orange oil, P.inspersa is characterised by the inspersed hamathecium; P.thailandicoides is characterised by the IKI+ red hamathecium and the existence of an unknown lichen substance; and P.apiculata is characterised by the absence of endospore layers in the spore tips and the absence of pseudocyphellae. It is reported for the first time that the presence of a gelatinous halo around the ascospores of Pyrenula is common. A word key for the Pyrenula species with red or orange oil in over-mature ascospores is provided.

Recent advances in the pre-oxidation process in electrocatalytic urea oxidation reactions.

The electrocatalytic urea oxidation reaction (UOR) has attracted substantial research interests over the past few years owing to its critical role in coupled electrochemical systems for energy conversion, for example, coupling with the hydrogen evolution reaction (HER) to realize urea-assisted hydrogen production and assembling direct urea fuel cells (DUFC) by coupling with the oxygen reduction reaction (ORR). The UOR process has been proved to be a two-step process which involves an electrochemical pre-oxidation reaction of the metal sites and a subsequent chemical oxidation of the urea molecules on the as-formed high-valence metal sites. Hence, designing advanced (pre-)catalysts with a boosted pre-oxidation reaction is of great importance in improving the UOR performance and thus accelerating the coupled reactions. In this feature article, we discuss the significant role of the pre-oxidation process during the urea electro-oxidation reaction, and summarize detailed strategies and recent advances in promoting the pre-oxidation reaction, including the modulation of the crystallinity, active phase engineering, defect engineering, elemental incorporation and constructing hierarchical nanostructures. We anticipate that this feature article will offer helpful guidance for the design and optimization of advanced (pre-)catalysts for UOR and related energy conversion applications.

Acceleration of the pre-oxidation process by tuning the degree of sulfurization for promoted oxygen evolution reaction.

In this work, Co-based nanocatalysts with variable degrees of sulfurization (DoS) were fabricated for the oxygen evolution reaction (OER). The partially sulfurized catalyst with a medium DoS could exhibit a promoted pre-oxidation process, leading to a highly efficient and ultrastable OER performance.

Cerium-induced lattice disordering in Co-based nanocatalysts promoting the hydrazine electro-oxidation behavior.

In this work, cerium-incorporated Co-based catalysts encapsulated in nitrogen-doped carbon were fabricated for the electrocatalytic hydrazine oxidation reaction (HzOR). The Ce incorporation could lead to the formation of surface oxide nanolayers with a disordered lattice, endowing the catalyst with enriched active sites and enhanced intrinsic activity for promoted HzOR.

A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk.

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

"Pit-dot" ultrathin nanosheets of hydrated copper pyrophosphate as efficient pre-catalysts for robust water oxidation.

Herein, hydrated copper pyrophosphate ultrathin nanosheets with a unique "pit-dot" nanostructure were fabricated as efficient pre-catalysts for the oxygen evolution reaction, and systematic post-catalytic characterization studies confirmed the important role of the boosted pre-oxidation reaction in promoting the OER catalysis.

A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations.

Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well.