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.

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method.

HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson-Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.

FOXO1 associated with sensitivity to chemotherapy drugs and glial-mesenchymal transition in glioma.

Mesenchymal subtype of glioblastoma (GBM), identified as one of four clinically relevant molecular subtypes, has worst prognosis because of its close relation with the malignant biological properties induced by glial-mesenchymal transition (GMT). However, the molecular mechanism of GMT and its characterized molecule of GBM have not been studied. Forkhead box protein O1 (FOXO1) is at a convergence point of receptor tyrosine kinase signaling as one of the three core pathways implicated in GBM. Our previous study indicated that the inactivation of FOXO1 involved in the inhibition of GMT is an independent prognosis factor of GBM. In this study, we will further confirm the role of FOXO1 in GMT through cytological experiments to clarify how FOXO1 regulates GMT and its clinical significance. We established virus-infected FOXO1 overexpression and FOXO1 knockdown cells of U373 MG and U251 mediated by lentivirus, based on the effect of which FOXO1-correlated-GMT experiments were performed in vitro and in vivo. Our data suggested that FOXO1 played a crucial role in resistance to TMZ, BCNU, and CDDP; migration and invasion; and stem cell properties of glioma cells. FOXO1 may serve as a targeted biomarker for prediction of sensitivity to chemotherapy drugs, metastasis, and prognosis, which provides a new idea for mesenchymal GBM treatment.

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.

BACKGROUND: Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS: We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred .

Integrated in silico-in vitro characterization, identification and disruption of the intermolecular interaction between SH3 domain-containing protein kinases and human pituitary tumor-transforming gene 1.

The human pituitary tumor-transforming gene-1 (hPTTG1) has been found to be overexpressed in various cancers. Accumulated evidences implicate that some of protein kinases can specifically recognize two PXXP motifs at hPTTG1 C-terminus through their Src homology (SH3) domain and then phosphorylate the protein by their catalytic domain. Here, we integrate in silico analysis and in vitro assay to characterize the intermolecular interaction between the two hPTTG1 motif peptides 161LGPPSPVK168 (M1P) and 168KMPSPPWE175 (M2P) and the SH3 domains of Ser/Thr-specific protein kinases MAP3K and PI3K. It is identified that the two peptides bind to MAP3K SH3 domain with a moderate affinity, but cannot form stable complexes with PI3K SH3 domain. Long time scale molecular dynamics (MD) simulations reveal that the M1P peptide can fold into a standard poly-proline II helix that is bound in the peptide-binding pocket of MAP3K SH3 domain, while the M2P peptide gradually moves out of the pocket during the simulations and finally forms a weak, transient encounter complex with the domain. All these suggest that the MAP3K M1P site is a potential interacting partner of MAP3K SH3 domain, which may mediate the intermolecular recognition between hPTTG1 and MAP3K.

Magnetic Co@g-C3N4 Core-Shells on rGO Sheets for Momentum Transfer with Catalytic Activity toward Continuous-Flow Hydrogen Generation.

Magnetic core-shell structures provide abundant opportunities for the construction of multifunctional composites. In this article, magnetic core-shells were fabricated with Co nanoparticles (NPs) as cores and g-C3N4 as shells. In the fabrication process, the Co@g-C3N4 core-shells were anchored onto the rGO nanosheets to form a Co@g-C3N4-rGO composite (CNG-I). For hydrogen generation from the hydrolysis of NaBH4 or NH3BH3, the Co NP cores act as catalytic active sites. The g-C3N4 shells protect Co NPs cores from aggregating or growing. The connection between Co NPs and rGO was strengthened by the g-C3N4 shells to prevent them from leaching or flowing away. The g-C3N4 shells also work as a cocatalyst for hydrogen generation. The magnetism of Co NPs and the shape of rGO nanosheets achieve effective momentum transfer in the external magnetic field. In the batch reactor, a higher catalytic activity was obtained for CNG-I in self-stirring mode than in magneton stirring mode. In the continuous-flow process, stable hydrogen generation was carried out with CNG-I being fixed and propelled by the external magnetic field. The separation film is unnecessary because of magnetic momentum transfer. This idea of the composite design and magnetic momentum transfer will be useful for the development of both hydrogen generation and multifunctional composite materials.

miR-372 regulates glioma cell proliferation and invasion by directly targeting PHLPP2.

MicroRNAs are known to be involved in carcinogenesis and tumor progression in glioma. Recently, microRNA-372 (miR-372) has been proved to play a substantial role in several human cancers, but its functions in glioma remain unclear. In this study, we confirmed that miR-372 was commonly upregulated in glioma cell lines and tissues. Downregulation of miR-372 markedly inhibited cell proliferation and invasion and induced G1/S arrest and apoptosis. Consistently, the xenograft mouse model also unveiled the suppressive effects of miR-372 knockdown on tumor growth. Further studies revealed that miR-372 modulated the expression of PHLPP2 by directly targeting its 3'-untranslated region (3'-UTR) and that miR-372 expression was inversely correlated with PHLPP2 expression in glioma samples. Silencing of PHLPP2 could rescue the inhibitory effect of miR-372 inhibitor. Moreover, miR-372 knockdown suppressed the phosphorylation levels of the major components of PI3K/Akt pathway including Akt, mTOR, and P70S6K. Taken together, our results suggest that miR-372 functions as an oncogenic miRNA through targeting PHLPP2 in glioma.

Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

The association between Salt-inducible kinase 2 (SIK2) and gamma isoform of the regulatory subunit B55 of PP2A (B55gamma) contributes to the survival of glioma cells under glucose depletion through inhibiting the phosphorylation of S6K.

BACKGROUND: PPP2R2C encodes a gamma isoform of the regulatory subunit B55 subfamily consisting PP2A heterotrimeric with A and C subunits. Currently, the precise functions of B55gamma in cancer are still under investigating. In this project, we reported a novel function of B55gamma in the regulation of glucose metabolism in Glioma cells. METHODS: Western blot and immunoprecipitation were performed to determine protein expression and interaction. Cell viability was measured by Typan Blue staining and direct cell counting using hematocytometer. siRNA technology was used to down regulate protein expression. RESULTS: Glucose uptake and lactate product were suppressed by overexpression of B55gamma in Glioma cells. In addition, cancer cells with larger amount of B55gamma showed higher survival advantages in response to glucose starvation through the dephosphorylation of S6K. From proteomic analysis, we found B55gamma binds with and up regulates SIK2 through the stabilization of SIK2 protein which is required for the B55gamma-mediated suppression of S6K pathway. Knocking down of SIK2 in B55gamma over expressing cells recovered the phosphorylation of S6K. CONCLUSION: In summary, our project will provide novel insight into the design and development of therapeutic strategies to target the B55gamma-mediated glucose metabolism for the treatment of human brain tumor patients.

The histone deacetylase SIRT6 suppresses the expression of the RNA-binding protein PCBP2 in glioma.

More than 80% of tumors that occur in the brain are malignant gliomas. The prognosis of glioma patients is still poor, which makes glioma an urgent subject of cancer research. Previous evidence and our present data show that PCBP2 is over-expressed in human glioma tissues and predicts poor outcome. However, the mechanism by which PCBP2 is regulated in glioma remains elusive. We find that SIRT6, one of the NAD(+)-dependent class III deacetylase SIRTUINs, is down-regulated in human glioma tissues and that the level of SIRT6 is negatively correlated with PCBP2 level while H3K9ac enrichment on the promoter of PCBP2 is positively correlated with PCBP2 expression. Furthermore, we identify PCBP2 as a target of SIRT6. We demonstrate that PCBP2 expression is inhibited by SIRT6, which depends upon deacetylating H3K9ac. Finally, our results reveal that SIRT6 inhibits glioma cell proliferation and colony formation in vitro and glioma cell growth in vivo in a PCBP2 dependent manner. In summary, our findings implicate that SIRT6 inhibits PCBP2 expression through deacetylating H3K9ac and SIRT6 acts as a tumor suppressor in human glioma.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC.

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php.