LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Precise Design and Modification Engineering of Single-Atom Catalytic Materials for Oxygen Reduction.

Due to their unique electronic and structural properties, single-atom catalytic materials (SACMs) hold great promise for the oxygen reduction reaction (ORR). Coordinating environmental and engineering strategies is the key to improving the ORR performance of SACMs. This review summarizes the latest research progress and breakthroughs of SACMs in the field of ORR catalysis. First, the research progress on the catalytic mechanism of SACMs acting on ORR is reviewed, including the latest research results on the origin of SACMs activity and the analysis of pre-adsorption mechanism. The study of the pre-adsorption mechanism is an important breakthrough direction to explore the origin of the high activity of SACMs and the practical and theoretical understanding of the catalytic process. Precise coordination environment modification, including in-plane, axial, and adjacent site modifications, can enhance the intrinsic catalytic activity of SACMs and promote the ORR process. Additionally, several engineering strategies are discussed, including multiple SACMs, high loading, and atomic site confinement. Multiple SACMs synergistically enhance catalytic activity and selectivity, while high loading can provide more active sites for catalytic reactions. Overall, this review provides important insights into the design of advanced catalysts for ORR.

Nucleosome positioning based on DNA sequence embedding and deep learning.

BACKGROUND: Nucleosome positioning is the precise determination of the location of nucleosomes on DNA sequence. With the continuous advancement of biotechnology and computer technology, biological data is showing explosive growth. It is of practical significance to develop an efficient nucleosome positioning algorithm. Indeed, convolutional neural networks (CNN) can capture local features in DNA sequences, but ignore the order of bases. While the bidirectional recurrent neural network can make up for CNN's shortcomings in this regard and extract the long-term dependent features of DNA sequence. RESULTS: In this work, we use word vectors to represent DNA sequences and propose three new deep learning models for nucleosome positioning, and the integrative model NP_CBiR reaches a better prediction performance. The overall accuracies of NP_CBiR on H. sapiens, C. elegans, and D. melanogaster datasets are 86.18%, 89.39%, and 85.55% respectively. CONCLUSIONS: Benefited by different network structures, NP_CBiR can effectively extract local features and bases order features of DNA sequences, thus can be considered as a complementary tool for nucleosome positioning.

Engineering Bimodal Oxygen Vacancies and Pt to Boost the Activity Toward Water Dissociation.

Water dissociation is the rate-limiting step of several energy-related reactions due to the high energy barrier required for breaking the oxygen-hydrogen bond. In this work, a bimodal oxygen vacancy (VO ) catalysis strategy is adopted to boost the efficient water dissociation on Pt nanoparticles. The single facet-exposed TiO2 surface and NiOx nanocluster possess two modes of VO different from each other. In ammonia borane hydrolysis, the highest catalytic activity among Pt-based materials is achieved with the turnover frequency of 618 min-1 under alkaline-free conditions at 298 K. Theoretical simulation and characterization analyses reveal that the bimodal VO significantly promotes the water dissociation in two ways. First, an ensemble-inducing effect of Pt and VO in TiO2 drives the activation of water molecules. Second, an electron promoter effect induced by the electron transfer from VO in NiOx to Pt further enhances the ability of Pt to dissociate water and ammonia borane. This insight into bimodal VO catalysis establishes a new avenue to rationally design heterogeneous catalytic materials in the energy chemistry field.

Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms.

BACKGROUND: Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning. RESULTS: Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved. CONCLUSIONS: Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.

Wood-Derived Integral Air Electrode for Enhanced Interfacial Electrocatalysis in Rechargeable Zinc-Air Battery.

Zinc-air batteries (ZABs) are promising as energy storage devices owing to their high energy density and the safety of electrolytes. Construction of abundant triple-phase boundary (TPB) effectively facilitates cathode reactions occurring at TPB. Herein, a wood-derived integral air electrode containing Co/CoO nanoparticles and nitrogen-doped carbonized wood (Co/CoO@NWC) is constructed with a dual catalytic function. The potential gap between oxygen reduction and evolution is shortened to 0.77 V. Liquid ZABs using Co/CoO@NWC as cathode exhibit high discharge specific capacity (800 mAh gZn-1 ), low charge-discharge gap (0.84 V), and long-term cycling stability (270 h). Co/CoO@NWC also shows distinguished catalytic activity and stability in all-solid-state ZABs. The inherent layered porous and pipe structures of wood are well maintained in catalytically active carbon. The different hydrophilicity of carbonized wood and Co/CoO endow abundant TPBs for battery reaction. The Co/CoO located on TPB provides main active sites for oxygen reactions. The inherent pipe structures of wood carbon and the interaction between Co/CoO and NWC effectively prevent nanoparticles from aggregation. The design and preparation of this monolithic electrocatalyst contribute to the broad-scale application of ZABs and promote the development of next-generation biomass-based storage devices.

Multiscale adaptive multifractal analysis and its applications.

To precisely analyze the fractal nature of a short-term time series under the multiscale framework, this study introduces multiscale adaptive multifractal analysis (MAMFA) combining the adaptive fractal analysis method with the multiscale multifractal analysis (MMA). MAMFA and MMA are both applied to the two kinds of simulation sequences, and the results show that the MAMFA method achieves better performances than MMA. MAMFA is also applied to the Chinese and American stock indexes and the R-R interval of heart rate data. It is found that the multifractal characteristics of stock sequences are related to the selection of the scale range s. There is a big difference in the Hurst surface's shape of Chinese and American stock indexes and Chinese stock indexes have more obvious multifractal characteristics. For the R-R interval sequence, we find that the subjects with abnormal heart rate have significant shape changes in three areas of Hurst surface compared with healthy subjects, thereby patients can be effectively distinguished from healthy subjects.

Randomized multifractal detrended fluctuation analysis of long time series.

A novel general randomized method is proposed to investigate multifractal properties of long time series. Based on multifractal temporally weighted detrended fluctuation analysis (MFTWDFA), we obtain randomized multifractal temporally weighted detrended fluctuation analysis (RMFTWDFA). The innovation of this algorithm is applying a random idea in the process of dividing multiple intervals to find the local trend. To test the performance of the RMFTWDFA algorithm, we apply it, together with the MFTWDFA, to the artificially generated time series and real genomic sequences. For three types of artificially generated time series, consistency tests are performed on the estimated h(q), and all results indicate that there is no significant difference in the estimated h(q) of the two methods. Meanwhile, for different sequence lengths, the running time of RMFTWDFA is reduced by over ten times. We use prokaryote genomic sequences with large scales as real examples, the results obtained by RMFTWDFA demonstrate that these genomic sequences show fractal characteristics, and we leverage estimated exponents to study phylogenetic relationships between species. The final clustering results are consistent with real relationships. All the results reflect that RMFTWDFA is significantly effective and timesaving for long time series, while obtaining an accuracy statistically comparable to other methods.

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies.

Genome-wide association study (GWAS) has turned out to be an essential technology for exploring the genetic mechanism of complex traits. To reduce the complexity of computation, it is well accepted to remove unrelated single nucleotide polymorphisms (SNPs) before GWAS, e.g., by using iterative sure independence screening expectation-maximization Bayesian Lasso (ISIS EM-BLASSO) method. In this work, a modified version of ISIS EM-BLASSO is proposed, which reduces the number of SNPs by a screening methodology based on Pearson correlation and mutual information, then estimates the effects via EM-Bayesian Lasso (EM-BLASSO), and finally detects the true quantitative trait nucleotides (QTNs) through likelihood ratio test. We call our method a two-stage mutual information based Bayesian Lasso (MBLASSO). Under three simulation scenarios, MBLASSO improves the statistical power and retains the higher effect estimation accuracy when comparing with three other algorithms. Moreover, MBLASSO performs best on model fitting, the accuracy of detected associations is the highest, and 21 genes can only be detected by MBLASSO in Arabidopsis thaliana datasets.

Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors.

In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial cross-sample entropy (CMPCSE), for quantifying the intrinsic similarity of two time series affected by common external factors. First, in order to test the validity of CMPCSE, we apply it to three sets of artificial data. Experimental results show that CMPCSE can accurately measure the intrinsic cross-sample entropy of two simultaneously recorded time series by removing the effects from the third time series. Then CMPCSE is employed to investigate the partial cross-sample entropy of Shanghai securities composite index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) by eliminating the effect of Hang Seng Index (HSI). Compared with the composite multiscale cross-sample entropy, the results obtained by CMPCSE show that SSEC and SZSE have stronger similarity. We believe that CMPCSE is an effective tool to study intrinsic similarity of two time series.