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Tissue engineering offers immense potential for addressing the unmet needs in repairing tissue damage and organ failure. Vascularization, the development of intricate blood vessel networks, is crucial for the survival and functions of engineered tissues. Nevertheless, the persistent challenge of ensuring an ample nutrient supply within implanted tissues remains, primarily due to the inadequate formation of blood vessels. This issue underscores the vital role of the human vascular system in sustaining cellular functions, facilitating nutrient exchange, and removing metabolic waste products. In response to this challenge, new approaches have been explored. Microfluidic devices, emulating natural blood vessels, serve as valuable tools for investigating angiogenesis and allowing the formation of microvascular networks. In parallel, bioprinting technologies enable precise placement of cells and biomaterials, culminating in vascular structures that closely resemble the native vessels. To this end, the synergy of microfluidics and bioprinting has further opened up exciting possibilities in vascularization, encompassing innovations such as microfluidic bioprinting. These advancements hold great promise in regenerative medicine, facilitating the creation of functional tissues for applications ranging from transplantation to disease modeling and drug testing. This review explores the potentially transformative impact of microfluidic and bioprinting technologies on vascularization strategies within the scope of tissue engineering. 

Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external knowledge sources, such as knowledge graphs, are often used to augment LMs. In this work, we propose knOwledge REasOning empowered Language Model (OREOLM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. In this way, LM guides KG to walk towards the desired answer, while the retrieved knowledge improves LM. By adopting OREOLM to RoBERTa and T5, we show significant performance gain, achieving state-of-art results in the Closed-Book setting. The performance enhancement is mainly from the KG reasoning’s capacity to infer missing relational facts. In addition, OREOLM provides reasoning paths as rationales to interpret the model’s decision. 

Abstract Prior observational uncertainties have hindered the clear understanding of the link between tropospheric Lamb waves and ionospheric disturbances. In this study, we precisely extracted ionospheric Lamb waves originating from the epicenter of the 15 January 2022 Tonga eruption, propagating upward in a conical structure. This was achieved by using line‐of‐sight observations from the BeiDou geostationary satellites, which eliminated the spatiotemporal ambiguity introduced by the relative motion of Global Positioning System satellites, enabling the clear extraction of the Lamb signal in the ionosphere. The observed L0 mode speed (∼323 m/s) and period (∼30 min) were consistent with those of the tropospheric Lamb wave. It suggested that the ionospheric Lamb wave is likely driven by the surface Lamb wave, leading to a conical wave‐front that extends in altitude. This study highlights the significant role of Lamb waves in transmitting energy from epicenters through Earth's atmosphere and plasma systems.