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Due to the uncertainty of trauma or infection, customized bone substitutes are often required in clinic. Meanwhile, excessive use of antibiotics may lead to drug resistance. Therefore, the design of anti-infection bone tissue engineering scaffold is of very important. In this study, porous titanium alloy bone tissue engineering scaffolds were designed and fabricated by 3D printing. TiO2 nanotubes were further constructed on the scaffolds through electrochemical anodic oxidation, achieving the drug loading and anti-infection functions. The micron-level bionic pores were fabricated by the 3D printing process, and the secondary nanoscale-level nanotubes were achieved through the anodic oxidation process. Thereafter, the micro–nano structured porous bone tissue engineering scaffolds are presented. This structure features that the drug release rate can be regulated by loading the anti-infection drug minocycline and coating them with poly(lactic-co-glycolic acid) (PLGA) in the nanotubes. According to the results, the micro–nano composite porous scaffold showed uniform and controllable micro–nano pores, it may load anti-infection drugs and shown anti-infection ability. In addition, the PLGA coating may delay the drug release and maintain a sustained anti-infection function for the scaffold in a week. This study provides new ideas for designing antibacterial bone tissue engineering scaffold.

Porous structures and surface morphology of bone tissue scaffolds play an important role in improving the biocompatibility and antibacterial properties for bone repair. In this study, we investigated the effect of different anodic oxidation parameters on the nanotubes morphology in 3D printed porous titanium scaffolds. Micron-scale pores were fabricated by 3D printing first, and then the nano-scale tubes were obtained via anodizing treatments. The results demonstrated that the morphology of the nanotubes depended on the anodic oxidation time and voltage, respectively. Longer anodic oxidation led to the formation of circle-like nanotubes, and the diameter of the nanotubes increased with the voltage. The scaffolds anodized at 30 V showed the best cell proliferation potential. The presence of nanotubes on the surface of scaffold altered the adhesion of bacteria so that it improved the antibacterial properties of scaffold. The formation of nanotubes improved the drug-loading ability of the scaffold, which are used for loading of minocycline antibacterial drugs. The proposed 3D printed porous Ti6Al4V scaffold with nanotubes surface modification showed obvious antibacterial effect, which is expected to have a promising application in antibacterial bone prosthesis.

Introduction: Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision‐making can increase the likelihood of a collision. Methods: A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20‐min period of sustained attention to locate a target vessel and (2) a 10‐min period of decision‐making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15‐channel functional near‐infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph‐theoretic measures. Results: Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision‐making. The graph‐theoretic analysis revealed that density declined during decision‐making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. Conclusions: The right lateral area of the PFC is sensitive to watchkeeping and decision‐making during operational performance. Graph‐theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety‐critical performance. [ABSTRACT FROM AUTHOR]

Reasoning over temporal knowledge graphs (TKGs) is a challenging task that requires models to infer future events based on past facts. Currently, subgraph-based methods have become the state-of-the-art (SOTA) techniques for this task due to their superior capability to explore local information in knowledge graphs (KGs). However, while previous methods have been effective in capturing semantic patterns in TKG, they are hard to capture more complex topological patterns. In contrast, path-based methods can efficiently capture relation paths between nodes and obtain relation patterns based on the order of relation connections. But subgraphs can retain much more information than a single path. Motivated by this observation, we propose a new subgraph-based approach to capture complex relational patterns. The method constructs candidate-oriented relational graphs to capture the local structure of TKGs and introduces a variant of a graph neural network model to learn the graph structure information between query-candidate pairs. In particular, we first design a prior directed temporal edge sampling method, which is starting from the query node and generating multiple candidate-oriented relational graphs simultaneously. Next, we propose a recursive propagation architecture that can encode all relational graphs in the local structures in parallel. Additionally, we introduce a self-attention mechanism in the propagation architecture to capture the query's preference. Finally, we design a simple scoring function to calculate the candidate nodes' scores and generate the model's predictions. To validate our approach, we conduct extensive experiments on four benchmark datasets (ICEWS14, ICEWS18, ICEWS0515, and YAGO). Experiments on four benchmark datasets demonstrate that our proposed approach possesses stronger inference and faster convergence than the SOTA methods. In addition, our method provides a relational graph for each query-candidate pair, which offers interpretable evidence for TKG prediction results.