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Aiming at the problem of big calculation error in solving reliability index by the traditional response surface method, an improved response surface method based on the linear gradient iteration criterion is proposed. First, the linear gradient iteration criterion is proposed to reduce the iteration step size with the increase of iteration times. It will improve the fitting accuracy of response surface and lead to a better convergence while approaching the limit state surface. Then, the reduction coefficient of the linear gradient iterative criterion is studied. The optimal value of coefficient is 0.2. The improved response surface method will get a more accurate reliability index quickly. Examples show that the proposed method has obvious advantages of high accuracy and efficiency. The application of this method can also be expanded in other similar engineering structure.

In structural reliability analysis, the HL-RF method may not converge in some nonlinear cases. The chaos control based first-order second-moment method (CC) achieves convergence by controlling the step length with chaotic control factors, but it commonly requires very time-consuming computation. In this paper, an Armijo-based hybrid step length release method based on chaos control is proposed to surmount the above issue. An iterative control angle is introduced for the proposed method to select an adaptive adjustment step length strategy. Then, a step length release method is proposed to speed up the convergence when the iterative rotation angle is less than the rotation control angle. When the iterative rotation angle exceeds the rotation control angle, an adaptive adjustment method for step length is defined based on the Armijo rule to provide an optimal choice of adaptive step length for the iterative process and guarantee convergence. After that, the robustness and efficiency of the proposed method are proved through several examples. The examples show that the proposed method is capable of generating a suitable adaptive step length, therefore accessing a more stable and accurate solution with greater efficiency in both high and low nonlinearity cases. It can well combine the advantages of HL-RF and the CC methods, and the efficiency is further improved without sacrificing its robustness. Finally, a discussion is brought out to investigate the selection of optimal parameters and how the two step length selection strategy cooperates and co-action with one another. It can be seen that the efficiency improvement of the proposed method mainly contributed to the step length release method, while the Armijo-based adaptive adjustment method for step length guaranteed convergence.

Erythrocytes are the dominant component of a blood clot in terms of volume and number. However, longstanding compacted erythrocytes in blood clots form a physical barrier and make fibrin mesh more anti‐fibrinolytic, thus impeding infiltration of mesenchymal stem cells. The necrosis or lysis of erythrocytes that are not removed timely can also lead to the release of pro‐inflammatory toxic metabolites, interfering with bone regeneration. Proper bio‐elimination of erythrocytes is essential for an undisturbed bone regeneration process. Here, hypoxia‐mimicking is applied to enhance macrophage‐elimination of erythrocytes. The effect of macrophage‐elimination of erythrocytes on the macrophage intracellular reaction, bone regenerative microenvironment, and bone regeneration outcome is investigated. Results show that the hypoxia‐mimicking agent dimethyloxalylglycine successfully enhances erythrophagocytosis by macrophages in a dose‐dependent manner primarily by up‐regulating the expression of integrin αvβ3. Increased phagocytosed erythrocytes then regulate macrophage intracellular Fe2+‐glycolysis‐inflammation, creating an improved bone regenerative microenvironment characterized by loose fibrin meshes with down‐regulated local inflammatory responses in vivo, thus effectively promoting early osteogenesis and ultimate bone generation. Modulating macrophage‐elimination of erythrocytes can be a promising strategy for eradicating erythrocyte‐caused bone regeneration hindrance and offers a new direction for advanced biomaterial development focusing on the bio‐elimination of erythrocytes. Longstanding compacted erythrocytes impair bone regeneration. This study employs the hypoxia‐mimetic agent DMOG to enhance macrophage‐elimination of erythrocytes by up‐regulating integrin αvβ3. Increased phagocytosed erythrocytes regulate macrophage intracellular Fe2+‐glycolysis‐inflammation, creating a favorable bone regenerative microenvironment characterized by loose fibrin meshes with down‐regulated inflammatory responses in vivo. These findings pave the way for advanced biomaterial development focusing on the bio‐elimination of erythrocytes.

Single-cell ribonucleic acid sequencing (scRNA-seq) allows researchers to study cell heterogeneity and diversity at the individual cell level. Cell clustering is an essential component of scRNA-seq data processing. However, the high dimensionality and high noise characteristics of scRNA-seq data may pose problems during data processing. Although many methods are available for scRNA-seq clustering analysis, most of them ignore the topological relationships of scRNA-seq data and do not fully utilize the potential associations between cells. In this study, we present scGAD, a graph attention autoencoder model with a dual decoder structure for clustering scRNA-seq data. We utilize a graph attention autoencoder with two decoders to learn feature representations of cells in latent space. To ensure that the learned latent feature representation maintains node properties and graph structure, we use an inner product decoder and a learnable graph attention decoder to reconstruct graph structure and node properties, respectively. On the 12 real scRNA-seq datasets, the average NMI and ARI scores of scGAD are 0.762 and 0.695, respectively, outperforming state-of-the-art single-cell clustering approaches. Biological analysis shows that the cell labels predicted by scGAD can assist in the downstream analysis of scRNA-seq data.

Recognizing drug–target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets. High-order information, as an essential component, exhibits complementarity with low-order information. Hence, the incorporation of higher-order associations between drugs and targets, while adequately integrating them with the existing lower-order information, could potentially yield substantial breakthroughs in predicting drug–target interactions. We propose a novel dual channels network-based learning model CHL-DTI that converges high-order information from hypergraphs and low-order information from ordinary graph for drug–target interaction prediction. The convergence of high–low order information in CHL-DTI is manifested in two key aspects. First, during the feature extraction stage, the model integrates both high-level semantic information and low-level topological information by combining hypergraphs and ordinary graph. Second, CHL-DTI fully fuse the innovative introduced drug–protein pairs (DPP) hypergraph network structure with ordinary topological network structure information. Extensive experimentation conducted on three public datasets showcases the superior performance of CHL-DTI in DTI prediction tasks when compared to SOTA methods. The source code of CHL-DTI is available at https://github.com/UPCLyy/CHL-DTI . Graphical abstract

The tripeptide Leu-Pro-Lys (LPK), derived from the Sipunculus nudus protein, was synthesized and studied to investigate its potential protective effect on bone formation. The effect and mechanism of LPK were analyzed through network pharmacology, bioinformatics, and experimental pharmacology. The study found that LPK at concentrations of 25 μg/mL and 50 μg/mL significantly increased ALP activity and mineralization in C3H10 cells. LPK also increased the expression of COL1A1 and promoted bone formation in zebrafish larvae. Network pharmacology predicted 148 interaction targets between LPK and bone development, and analysis of the protein-protein interaction network identified 13 hub genes, including ESR1, MAPK8, and EGFR, involved in bone development. Through KEGG enrichment pathways analysis, it was determined that LPK promotes bone development by regulating endocrine resistance, the relaxin signaling pathway, and the estrogen signaling pathway. Molecular docking results showed direct interactions between LPK and ESR1, MAPK8, and MAPK14. Additional verification experiments using western blot assay revealed that LPK significantly upregulated the expression of genes related to bone formation, including COL1A1, OPG, RUNX2, ESR1, phosphorylated MAPK14, and phosphorylated MAPK8 in C3H10 cells. These results suggest that LPK promotes bone formation by activating the estrogen/MAPK signaling pathway.

Elasticity is a fundamental mechanical property of two-dimensional (2D) materials, and is critical for their application as well as for strain engineering. However, accurate measurement of the elastic modulus of 2D materials remains a challenge, and the conventional suspension method suffers from a number of drawbacks. In this work, we demonstrate a method to map the in-plane Young’s modulus of mono- and bi-layer MoS2 on a substrate with high spatial resolution. Bimodal atomic force microscopy is used to accurately map the effective spring constant between the microscope tip and sample, and a finite element method is developed to quantitatively account for the effect of substrate stiffness on deformation. Using these methods, the in-plane Young’s modulus of monolayer MoS2 can be decoupled from the substrate and determined as 265 ± 13 GPa, broadly consistent with previous reports though with substantially smaller uncertainty. It is also found that the elasticity of mono- and bi-layer MoS2 cannot be differentiated, which is confirmed by the first principles calculations. This method provides a convenient, robust and accurate means to map the in-plane Young’s modulus of 2D materials on a substrate.

Erythrocytes are the dominant component of a blood clot in terms of volume and number. However, longstanding compacted erythrocytes in blood clots form a physical barrier and make fibrin mesh more anti‐fibrinolytic, thus impeding infiltration of mesenchymal stem cells. The necrosis or lysis of erythrocytes that are not removed timely can also lead to the release of pro‐inflammatory toxic metabolites, interfering with bone regeneration. Proper bio‐elimination of erythrocytes is essential for an undisturbed bone regeneration process. Here, hypoxia‐mimicking is applied to enhance macrophage‐elimination of erythrocytes. The effect of macrophage‐elimination of erythrocytes on the macrophage intracellular reaction, bone regenerative microenvironment, and bone regeneration outcome is investigated. Results show that the hypoxia‐mimicking agent dimethyloxalylglycine successfully enhances erythrophagocytosis by macrophages in a dose‐dependent manner primarily by up‐regulating the expression of integrin α v β 3 . Increased phagocytosed erythrocytes then regulate macrophage intracellular Fe 2+ ‐glycolysis‐inflammation, creating an improved bone regenerative microenvironment characterized by loose fibrin meshes with down‐regulated local inflammatory responses in vivo, thus effectively promoting early osteogenesis and ultimate bone generation. Modulating macrophage‐elimination of erythrocytes can be a promising strategy for eradicating erythrocyte‐caused bone regeneration hindrance and offers a new direction for advanced biomaterial development focusing on the bio‐elimination of erythrocytes.

In recent years, distributed generation technology develops rapidly due to its' flexible and environment-friendly nature, and the wide application of electric vehicles poses a challenge to the safety of the system. In order to better analyze the impact of DG on the economics and safety of distribution networks, a probability model for random load, micro gas turbines and photovoltaic power generation system is formulated. With the objective of minimizing the network loss, lowest static insecurity probability, and the lowest cost of purchasing electricity, the optimization of distribution network with distributed generation is carried out by adjusting the distribution network topology and the output of controllable DG. The stochastic power flow is combined with the particle swarm optimization algorithm to obtain the Pareto non-inferior solution set, and then the subject is selected to obtain the optimal solution. Finally, simulations are carried out on the IEEE 33-bus test system and it is shown that the proposed method can effectively reduce the network loss and static insecurity probability on the basis of low cost of purchasing electricity.

In this study, a full-scale test on the bending performance of concrete sandwich laminated floor slabs with integrated thermal and sound insulation was carried out, and the effects of different reinforcement ratios on the bending performance of concrete sandwich laminated floor slabs were investigated as well as the variation law of the failure modes, characteristic loads, load-mid span deflection, load-rebar strain curves, and anti-slip performance. The results indicate that the concrete sandwich laminated floor slabs present typical bending failure characteristics. According to bending failure characteristics, the damage process can be divided into three stages, i.e., elasticity, cracking, and failure. The bearing capacity significantly increases with the increase in reinforcement ratio. The normal service, yield, and ultimate loads of bearing capacity of the floor slabs with a larger reinforcement ratio increase by 54.55%, 52.94%, and 46.46%, respectively. Moreover, the mid-span deflection decreases significantly with the increase in reinforcement ratio, and the cracking expansion is also delayed. Before cracking, the prefabricated layer and laminated layer can realize load bearing together, and the floor slab is in a state of complete interaction. When the floor slabs reach the ultimate state, the superimposed surface produces a sliding effect, and the floor slab is in a state of partial interaction. The finite element analysis software ABAQUS (with the version number of ABAQUS 2020, the chief creator of David Hibbitt, and the sourced location of the United States) was used to perform nonlinear numerical simulation. The test results accord well with the simulation results, which verifies the correctness of the finite element model. Based on finite element simulation, the influence of post-cast concrete strength on the ultimate load can be ignored.