Is there a stronger willingness to pay for photovoltaic power generation with high education in China?

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China.

Motif-Aware miRNA-Disease Association Prediction Via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives. The data and codes are available at https://github.com/stevejobws/MotifMDA.

Dislocation Majorana bound states in iron-based superconductors.

We show that lattice dislocations of topological iron-based superconductors such as FeTe1-xSex will intrinsically trap non-Abelian Majorana quasiparticles, in the absence of any external magnetic field. Our theory is motivated by the recent experimental observations of normal-state weak topology and surface magnetism that coexist with superconductivity in FeTe1-xSex, the combination of which naturally achieves an emergent second-order topological superconductivity in a two-dimensional subsystem spanned by screw or edge dislocations. This exemplifies a new embedded higher-order topological phase in class D, where Majorana zero modes appear around the "corners" of a low-dimensional embedded subsystem, instead of those of the full crystal. A nested domain wall theory is developed to understand the origin of these defect Majorana zero modes. When the surface magnetism is absent, we further find that s± pairing symmetry itself is capable of inducing a different type of class-DIII embedded higher-order topology with defect-bound Majorana Kramers pairs. We also provide detailed discussions on the real-world material candidates for our proposals, including FeTe1-xSex, LiFeAs, ß-PdBi2, and heterostructures of bismuth, etc. Our work establishes lattice defects as a new venue to achieve high-temperature topological quantum information processing.

Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Finite-momentum Cooper pairing in proximitized altermagnets.

Finite-momentum Cooper pairing is an unconventional form of superconductivity that is widely believed to require finite magnetization. Altermagnetism is an emerging magnetic phase with highly anisotropic spin-splitting of specific symmetries, but zero net magnetization. Here, we study Cooper pairing in metallic altermagnets connected to conventional s-wave superconductors. Remarkably, we find that the Cooper pairs induced in the altermagnets acquire a finite center-of-mass momentum, despite the zero net magnetization in the system. This anomalous Cooper-pair momentum strongly depends on the propagation direction and exhibits unusual symmetric patterns. Furthermore, it yields several unique features: (i) highly orientation-dependent oscillations in the order parameter, (ii) controllable 0-π transitions in the Josephson supercurrent, (iii) large-oblique-angle Cooper-pair transfer trajectories in junctions parallel with the direction where spin splitting vanishes, and (iv) distinct Fraunhofer patterns in junctions oriented along different directions. Finally, we discuss the implementation of our predictions in candidate materials such as RuO2 and KRu4O8.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Dirac-fermion-assisted interfacial superconductivity in epitaxial topological-insulator/iron-chalcogenide heterostructures.

Over the last decade, the possibility of realizing topological superconductivity (TSC) has generated much excitement. TSC can be created in electronic systems where the topological and superconducting orders coexist, motivating the continued exploration of candidate material platforms to this end. Here, we use molecular beam epitaxy (MBE) to synthesize heterostructures that host emergent interfacial superconductivity when a non-superconducting antiferromagnet (FeTe) is interfaced with a topological insulator (TI) (Bi, Sb)2Te3. By performing in-vacuo angle-resolved photoemission spectroscopy (ARPES) and ex-situ electrical transport measurements, we find that the superconducting transition temperature and the upper critical magnetic field are suppressed when the chemical potential approaches the Dirac point. We provide evidence to show that the observed interfacial superconductivity and its chemical potential dependence is the result of the competition between the Ruderman-Kittel-Kasuya-Yosida-type ferromagnetic coupling mediated by Dirac surface states and antiferromagnetic exchange couplings that generate the bicollinear antiferromagnetic order in the FeTe layer.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks.

BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Enhancing the conversational agent with an emotional support system for mental health digital therapeutics.

As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder.

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

Topological superconducting vortex from trivial electronic bands.

Superconducting vortices are promising traps to confine non-Abelian Majorana quasi-particles. It has been widely believed that bulk-state topology, of either normal-state or superconducting ground-state wavefunctions, is crucial for enabling Majorana zero modes in solid-state systems. This common belief has shaped two major search directions for Majorana modes, in either intrinsic topological superconductors or trivially superconducting topological materials. Here we show that Majorana-carrying superconducting vortex is not exclusive to bulk-state topology, but can arise from topologically trivial quantum materials as well. We predict that the trivial bands in superconducting HgTe-class materials are responsible for inducing anomalous vortex topological physics that goes beyond any existing theoretical paradigms. A feasible scheme of strain-controlled Majorana engineering and experimental signatures for vortex Majorana modes are also discussed. Our work provides new guidelines for vortex-based Majorana search in general superconductors.

PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel.

Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.

PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.

Identifying protein targets for drugs establishes an indispensable knowledge foundation for drug repurposing and drug development. Though expensive and time-consuming, vitro trials are widely employed to discover drug targets, and the existing relevant computational algorithms still cannot satisfy the demand for real application in drug R&D with regards to the prediction accuracy and performance efficiency, which are urgently needed to be improved. To this end, we propose here the PPAEDTI model, which uses the graph personalized propagation technique to predict drug-target interactions from the known interaction network. To evaluate the prediction performance, six benchmark datasets were used for testing with some state-of-the-art methods compared. As a result, using the 5-fold cross-validation, the proposed PPAEDTI model achieves average AUCs>90% on 5 collected datasets. We also manually checked the top-20 prediction list for 2 proteins (hsa:775 and hsa:779) and a kind of drug (D00618), and successfully confirmed 18, 17, and 20 items from the public datasets, respectively. The experimental results indicate that, given known drug-target interactions, the PPAEDTI model can provide accurate predictions for the new ones, which is anticipated to serve as a useful tool for pharmacology research. Using the proposed model that was trained with the collected datasets, we have built a computational platform that is accessible at http://120.77.11.78/PPAEDTI/ and corresponding codes and datasets are also released.

Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity.

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA-disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns.

Predicting Drug-Target Interactions Over Heterogeneous Information Network.

Identifying Drug-Target Interactions (DTIs) is a critical step in studying pathogenesis and drug development. Due to the fact that conventional experimental methods usually suffer from high costs and low efficiency, various computational methods have been proposed to detect potential DTIs by extracting features from the biological information of drugs and their target proteins. Though effective, most of them fall short of considering the topological structure of the DTI network, which provides a global view to discover novel DTIs. In this paper, a network-based computational method, namely LG-DTI, is proposed to accurately predict DTIs over a heterogeneous information network. For drugs and target proteins, LG-DTI first learns not only their local representations from drug molecular structures and protein sequences, but also their global representations by using a semi-supervised heterogeneous network embedding method. These two kinds of representations consist of the final representations of drugs and target proteins, which are then incorporated into a Random Forest classifier to complete the task of DTI prediction. The performance of LG-DTI has been evaluated on two independent datasets and also compared with several state-of-the-art methods. Experimental results show the superior performance of LG-DTI. Moreover, our case study indicates that LG-DTI can be a valuable tool for identifying novel DTIs.

Is there a stronger willingness to pay for air quality improvement with high education: new evidence from a survey in China.

As a developing country with the largest population and serious environmental pollution in the world, China has made great efforts in air pollution. Air quality improvement depends not only on government administrative regulations but also on public support, especially how much the public is willing to pay for air quality improvement. Higher education will encourage the public to take action to improve air quality. However, the confirmation of the causality relationship between WTP and education has been missing. This study uses the Chinese General Social Survey (CGSS) to find the relationship between the two, and the conclusions are drawn: OLS regression model and instrumental variable both determine the positive influence of education level on air quality improvement WTP, and Heckman model further verifies the robustness of the conclusion. The positive influence of education level is greater in the groups of men, higher income, higher awareness of acid rain, and more air purifiers, and it has a greater impact on married people in rural areas than in urban areas. The function mechanism of education can improve residents' WTP by increasing regional GDP, promoting urbanization level, expanding afforestation areas, decreasing private car ownership and the number of newly registered civil cars, and reducing sulfur dioxide emissions, nitrogen oxides, and smoke (powder) dust. The total social and economic value of air quality improvement in China is 34.572 billion CNY to 672.42 trillion CNY.