Search

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]

The exploration of highly efficient noble metal-free catalysts for overall water splitting in alkaline water/seawater at large-current density is critical for industrial application. In this work, FeCo-MOF derived hierarchical Co 4 S 3 /Fe 3 S 4 /MoS 2 nanosheet arrays on iron foam (IF) were prepared as efficient electrocatalyst for both HER and OER by solvothermal method. The CFM 0.3 nanosheet arrays exhibited the high HER performance with the Tafel slope of 70 mV dec-1 and extremely low overpotential of 241, 377 mV at 100 and 500 mA/cm2. The CFM 0.7 nanosheet arrays can deliver a high current density of 100 and 500 mA/cm2 at the overpotential as low as 220 and 305 mV and small Tafel slope (79 mV dec-1) for OER. Moreover, CFM0.3/CFM0.7 exhibited the low overpotentials of 270/380 mV for HER/OER at 100 mA/cm2 in simulated seawater. The CFM0.3//CFM0.7 delivered comparatively low cell voltage of 1.26/1.45 V at 10 mA/cm2 with excellent stability for 48 h in alkaline water/simulated seawater. Benefiting from the three-dimensional nanosheet arrays framework and the interface coupling interactions of trimetallic sulfides, the self-supporting robustness electrodes with abundant multimetallic electroactive centers can regulate charge/ion diffusion path and electronic structure for prompting HER and OER activity at high current density. Hence, this work would provide new ideas for exploration of highly-active MOF-derived non-noble metal bifunctional catalysts for energy-related applications. The Co 4 S 3 /Fe 3 S 4 /MoS 2 (CFM) nanosheet arrays on IF were prepared as an efficient electrocatalyst for both HER and OER. CFM 0.3 exhibited low overpotential of 241, 377 mV at 100 and 500 mA/cm2 for HER. CFM 0.7 showed low overpotential of 220 and 305 mV at 100 and 500 mA/cm2 for OER. CFM 0.3 and CFM 0.7 exhibited the low HER and OER overpotentials of 270 and 380 mV at 100 mA cm−2 in simulated seawater. CFM 0.3//CFM 0.7 delivered comparatively low cell voltages of 1.26 and 1.45 V at 10 mA/cm2 for overall water/seawater splitting. [Display omitted] • Co 4 S 3 /Fe 3 S 4 /MoS 2 nanosheet arrays were fabricated on IF by solvothermal method. • CFM0.3 exhibited 241(270)/377 mV at 100/500 mA/cm2 in 1.0 M KOH(seawater) for HER. • CFM0.7 exhibited 220(380)/305 mV at 100/500 mA/cm2 in 1.0 M KOH(seawater) for OER. • CFM0.3//CFM0.7 exhibited 1.26(1.45V) at 10 mA/cm2 for 48 h in 1.0 M KOH(seawater). [ABSTRACT FROM AUTHOR]

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks. Author summary: Drugs containing the same functional groups may have similar pharmacochemical properties. However, how to effectively combine chemical information of drugs from molecular fragments containing functional groups into the biomolecular network is challenging and rarely explored. we decompose drugs' SMILES string and construct a drug-centric heterogeneous network that integrates drug substructure and molecular interactions information. Based on the heterogeneous network, we proposed an end-to-end hypergraph attention network framework for the drug multi-task predictions, termed as HGDrug. The efficiency and generalization of the proposed HGDrug have been demonstrated by the state-of-the-art performance in four drug-related interaction predictions tasks with huge improvement compared to previous general-purpose classical models and task-specific models. In addition, HGDrug can effectively identify potential drug-related interactions and the drug-sub-structure networks are able to help to improve the performance of other GNN models. These conclusions present important insights on how to introduce the drug' substructure information for multiple drug-related interactions tasks on biomedical networks. In summary, HGDrug offers a general and powerful tool for the identification of drug-related interactions by constructing the micro-to-macro drug-centric heterogeneous network. [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]

Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model. Informative molecular representation is a vital prerequisite in artificial intelligence-driven de novo drug discovery, however, mapping the pharmacophoric information is underexploited by the atom-level based molecular graph representation. Here, the authors design a multi-level based Pharmacophoric-constrained heterogeneous graph transformer (PharmHGT) to better capture the pharmacophore structure and chemical information. [ABSTRACT FROM AUTHOR]

The latest biological findings observe that the motionless "lock‐and‐key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor. [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]

Motivation Drug–drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions. Results To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction. Availabilityand implementation The source code and data are available at https://github.com/Azra3lzz/LaGAT. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

• Increasing work function of p -doped layer increases open-circuit voltage. • Two combined p -doped layers increase fill factor. • Two combined p -doped layers increase short-circuit current density. • Two combined p -doped layers increase power conversion efficiency than single ones. Inverted organic solar cells (OSCs) have been fabricated using MoO 3 doped N,N' -bis-(1-naphthyl)-diphenyl-1,1′-biphenyl-4,4′-diamine (NPB:MoO 3), MoO 3 doped 4,4′- N,N' -dicarbazole-biphenyl (CBP:MoO 3), and combined CBP:MoO 3 /NPB:MoO 3 as anode modifying layers (AMLs), respectively. The CBP:MoO 3 /NPB:MoO 3 /anode increases fill factor of device than the NPB:MoO 3 /anode and CBP:MoO 3 /anode, mostly because the former enhances hole injection into photoactive layer than the latter two. The CBP:MoO 3 /NPB:MoO 3 /anode and CBP:MoO 3 /anode increase open-circuit voltage of device than the NPB:MoO 3 /anode, due to the larger work function of CBP:MoO 3 than that of NPB:MoO 3. It is also found that combined CBP:MoO 3 /NPB:MoO 3 enhances short-circuit current density of device than single NPB:MoO 3 and CBP:MoO 3 , mainly ascribed to the fact that the former improves absorptance of device than the latter two. As a result, the CBP:MoO 3 /NPB:MoO 3 /anode improves power conversion efficiency of device than the NPB:MoO 3 /anode and CBP:MoO 3 /anode. We present a useful concept to develop high-performance AMLs for inverted OSCs. [ABSTRACT FROM AUTHOR]

Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways. Disease comorbidity analysis from microRNA regulatory networks: Identification of disease–disease relationships play essential roles in development of personalized approaches to managing those conditions. A team led by Dr. Feixiong Cheng at Cleveland Clinic developed a network-based methodology (termed mpDisNet) to infer clinically relevant disease–disease relationships from miRNA regulatory networks. mpDisNet shows high performance in identification of clinically reported disease–disease relationships by a unique integration of miRNA–gene–disease networks under the human interactome model. Importantly, mpDisNet successfully identifies a network-based relationship between asthma and chronic obstructive pulmonary disease driven by underlying miRNA-mediated pathobiological pathways. From a translational perspective, if broadly applied, mpDisNet would offer a powerful network-based tool for understanding of clinical comorbidities for multiple complex diseases from heterogeneous biological networks, a significant challenge of precision medicine. [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]

Phosphorescent organic light emitting diodes (PHOLEDs) have been fabricated with structure of indium tin oxide/MoO3 doped 4,4′-N,N'-dicarbazole-biphenyl (CBP) 30 nm/tris(4-carbazoyl-9-ylphenyl)amine 10 nm/CBP doped with tris(2-phenylpyridine)iridium(III) (CBP:Ir(ppy)3) x/bathocuproine 50 nm/LiF 1 nm/Al, where x = 2.5, 5, 10, and 20 nm, respectively. The current efficiency (CE) of device with x = 10 nm is higher than those with x = 2.5 and 5 nm, mostly because the width of exciton formation zone (5.7 nm) with x = 10 nm is larger than those (2.5 and 5 nm) with x = 2.5 and 5 nm. However, the current density with x = 10 nm decreases than those with x = 2.5 and 5 nm at a certain driving voltage, since the ~ 4.3 nm CBP:Ir(ppy)3 accommodating no exciton formation with x = 10 nm plays a role of transporting holes, raising ohmic loss of hole and thereby increasing driving voltage. When x increases from 10 to 20 nm, the width of exciton formation zone rises from 5.7 to 6.8 nm with CE almost unchanged, and the current density decreases as a result of increased ohmic loss of hole. The current research is useful to develop high-efficiency and low-driving voltage PHOLEDs. [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]

With the graying of Chinese society increasing, elderly people who do not live with their family's younger generations has become common. Social and cultural factors have powerful psychological implications for elders in residential location choice making, with the premise of affordability. This preliminary study, is one of the first to examine systematic social and cultural factors of residential location choice, and classifies senior citizens by pre-retirement occupation. Survey data were obtained via three in-depth interviews from 2013 to 2015, and a large-scale questionnaire survey from August 15 to September 16 in 2015. Structural equation modeling results show that perception of comfort, perception of presence, and perception of convenience have marked effects, and perception of affection is not conspicuous on decision-making. Moreover, there are differences in social and cultural factors of elders according to different age periods and pre-retirement occupations. These findings highlight social and cultural demands by elders in residential location choices, and provides a basis for subsequent study on elderly dwelling location choice. [ABSTRACT FROM AUTHOR]

Identifying new lead molecules to treat cancer requires more than a decade of dedicated effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is generally validated by in vitro cellular experiments. Therefore, accurate prediction of cancer drug response is a critical and challenging task for anti-cancer drugs design and precision medicine. With the development of pharmacogenomics, the combination of efficient drug feature extraction methods and omics data has made it possible to use computational models to assist in drug response prediction. In this study, we propose DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses. Specifically, DeepTTA uses transcriptomic gene expression data and chemical substructures of drugs for drug response prediction. Compared to existing methods, DeepTTA achieved higher performance in terms of root mean square error, Pearson correlation coefficient and Spearman's rank correlation coefficient on multiple test sets. Moreover, we discovered that anti-cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications. With its excellent performance, DeepTTA is expected to be an effective method in cancer drug design. [ABSTRACT FROM AUTHOR]

Drug–target interactions (DTIs) prediction research presents important significance for promoting the development of modern medicine and pharmacology. Traditional biochemical experiments for DTIs prediction confront the challenges including long time period, high cost and high failure rate, and finally leading to a low-drug productivity. Chemogenomic-based computational methods can realize high-throughput prediction. In this study, we develop a deep collaborative filtering prediction model with multiembeddings, named DCFME (deep collaborative filtering prediction model with multiembeddings), which can jointly utilize multiple feature information from multiembeddings. Two different representation learning algorithms are first employed to extract heterogeneous network features. DCFME uses the generated low-dimensional dense vectors as input, and then simulates the drug–target relationship from the perspective of both couplings and heterogeneity. In addition, the model employs focal loss that concentrates the loss on sparse and hard samples in the training process. Comparative experiments with five baseline methods show that DCFME achieves more significant performance improvement on sparse datasets. Moreover, the model has better robustness and generalization capacity under several harder prediction scenarios. [ABSTRACT FROM AUTHOR]

Helminth infections in children are associated with impaired cognitive development; however, the biological mechanisms for this remain unclear. Using a murine model of gastrointestinal helminth infection, we demonstrate that early-life exposure to helminths promotes local and systemic inflammatory responses and transient changes in the gastrointestinal microbiome. Behavioral and cognitive analyses performed 9-months postinfection revealed deficits in spatial recognition memory and an anxiety-like behavioral phenotype in worm-infected mice, which was associated with neuropathology and increased microglial activation within the brain. This study demonstrates a previously unrecognized mechanism through which helminth infections may influence cognitive function, via perturbations in the gut-immune-brain axis. [ABSTRACT FROM AUTHOR]

Helminth infections in children are associated with impaired cognitive development; however, the biological mechanisms for this remain unclear. Using a murine model of gastrointestinal helminth infection, we demonstrate that early-life exposure to helminths promotes local and systemic inflammatory responses and transient changes in the gastrointestinal microbiome. Behavioral and cognitive analyses performed 9-months postinfection revealed deficits in spatial recognition memory and an anxiety-like behavioral phenotype in worm-infected mice, which was associated with neuropathology and increased microglial activation within the brain. This study demonstrates a previously unrecognized mechanism through which helminth infections may influence cognitive function, via perturbations in the gut-immune-brain axis. [ABSTRACT FROM AUTHOR]