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A spike-like double yolk–shell structured TiO2@ZnIn2S4 (D-Y-TiO2@ZnIn2S4) photocatalyst was designed, which possesses superior photocatalytic CO2 reduction efficiency.

Motivation Identification of Drug–Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart. Results We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug–protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction. Availability and implementation The research shows that this method has certain applicability in the discovery of drugs, the identification of drug–target pairs and so on.

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

A two-dimensional (2D) metal-organic framework (MOF) Na2Zn2(C9H3O6)2(H2O)8·3H2O has been synthesized based on the principal of stabilizing organic ligand by crystallization and coordination effect in MOFs. It exhibits unique multi-emissive room temperature phosphorescence (RTP) centered at 455 nm, 482 nm and 525 nm with lifetimes of 92 ms, 97 ms and 351 ms, respectively. Such Zn-based 2D MOF also shows temperature-responsive photoluminescence performance.

An efficient palladium-catalyzed cascade reaction of azides with isonitrile and amines is presented; it offers an alternative facile approach toward N-sulfonyl-, N-phosphoryl-, and N-acyl-functionalized guanidines in excellent yield. These series of substituted guanidines exhibit potential biological and pharmacological activities. In addition, the less reactive intermediate benzoyl carbodiimide could be isolated by silica gel column flash chromatography in moderate yield.

The first phosphine-catalyzed highly enantioselective [3 + 3] cycloaddition of Morita–Baylis–Hillman carbonates with C,N-cyclic azomethine imines is described. Using a spirocyclic chiral phosphine as the catalyst, a novel class of pharmaceutically interesting 4,6,7,11b-tetrahydro-1H-pyridazino[6,1-a]iso-quinoline derivatives were obtained in high yields with good to excellent diastereoselectivities and extremely excellent enantioselectivities (98–>99% ee).

The binaphthol-derived phosphoric acid-promoted [3 + 3] cycloaddition of azomethine ylides with quinone monoimine has been achieved in the presence of 3 A MS at room temperature, providing dihydrobenzoxazine derivatives in moderate to excellent yields.

As the uplink resource and the downlink resource are separated to some extent, conventional resource allocation algorithms usually treat the total system resource allocation independently in wireless networks. However, for the interactive service, its performance is mainly determined by the service quality of uplink and that of downlink jointly, and it is important to allocate the uplink and downlink resource simultaneously. In this paper, we propose a new joint uplink/downlink resource allocation algorithm in orthogonal frequency division multiple access (OFDMA) wireless networks with interactive service. The uplink and downlink resource are allocated jointly to improve the system performance, and the issue is formulated as an optimization problem. The paper presents the solving processes of the optimization problem by dual optimization method. The results of simulation indicate that the proposed joint uplink/downlink resource allocation algorithm can effectively promote the resource efficiency of the OFDMA wireless networks with interactive service.

Amidine is a notable nitrogen-containing structural motif found in bioactive natural products and pharmaceuticals. Herein, a novel rhodium(I)-catalyzed tandem reaction of readily accessible azides with isonitriles and boronic acids via a carbodiimide intermediate is achieved. This protocol offers an alternative approach toward N-sulfonyl-, N-acyl-, and N- phosphoryl-functionalized, as well as general N-aryl and N-alkyl amidines with broad substrate scope. In addition, functionalized guanidines can also been synthesized when amines are used instead. The accomplishment of estrone-derived amidine and glibenclamide bioisosteres further reveals the practical utility of this strategy.

The asymmetric [3 + 3] cycloaddition of C,N-cyclic azomethine imines with aryl-substituted Morita—Hillman carbonates (II, IV) is developed using a spirocyclic chiral phosphine as the catalyst.

In order to explore the interaction between the sea and monsoon in the South China Sea, the heat exchanges at air-sea interface during monsoon periods in 1986 were calculated using observational data. It shows that when the summer monsoon bursts and prevails over the South China Sea, the air-sea interface heat exchange is strong and the latent heat rises rapidly in the intertropical convergence zone and the tropic cyclone system near 20.49°N, 114.14°E. On May 24, 1986, the sensible heat became positive in the typhoon system. The heating exchange indicates that heat is transported from ocean to atmosphere, with major contribution of latent heat. When the summer monsoon prevails over the South China Sea and the weather is fine, even SST (sea surface temperature) is high, but sensible heat appears to be negative. The heat exchange indicates that heat is transported from atmosphere to ocean, with major contribution of short-wave radiation absorbed by sea surface and sensible heat. When summer monsoon is over and the northeast monsoon prevails over the South China Sea, the heat exchange at air-sea interface is very strong. The heating exchange shows that the ocean heats the atmosphere, with major contribution of latent heat when cold air arrives at the sea surface and the sensible heat rises to positive rapidly. Therefore it can be concluded that the heat exchange at air-sea interface is different from the SST in South China Sea. When the summer monsoon prevails over the South China Sea, the main trend is the ocean responding to the atmosphere.