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Droplet‐based bioprinting has shown remarkable potential in tissue engineering and regenerative medicine. However, it requires bioinks with low viscosities, which makes it challenging to create complex 3D structures and spatially pattern them with different materials. This study introduces a novel approach to bioprinting sophisticated volumetric objects by merging droplet‐based bioprinting and cryobioprinting techniques. By leveraging the benefits of cryopreservation, we fabricated, for the first time, intricate, self‐supporting cell‐free or cell‐laden structures with single or multiple materials in a simple droplet‐based bioprinting process that is facilitated by depositing the droplets onto a cryoplate followed by crosslinking during revival. The feasibility of this approach is demonstrated by bioprinting several cell types, with cell viability increasing to 80%–90% after up to 2 or 3 weeks of culture. Furthermore, the applicational capabilities of this approach are showcased by bioprinting an endothelialized breast cancer model. The results indicate that merging droplet and cryogenic bioprinting complements current droplet‐based bioprinting techniques and opens new avenues for the fabrication of volumetric objects with enhanced complexity and functionality, presenting exciting potential for biomedical applications. 

Three‐dimensional (3D) printing is an emerging technique that has shown promising success in engineering human tissues in recent years. Further development of vat‐photopolymerization printing modalities has significantly enhanced the complexity level for 3D printing of various functional structures and components. Similarly, the development of microfluidic chip systems is an emerging research sector with promising medical applications. This work demonstrates the coupling of a digital light processing (DLP) printing procedure with a microfluidic chip system to produce size‐tunable, 3D‐printable porosities with narrow pore size distributions within a gelatin methacryloyl (GelMA) hydrogel matrix. It is found that the generation of size‐tunable gas bubbles trapped within an aqueous GelMA hydrogel‐precursor can be controlled with high precision. Furthermore, the porosities are printed in two‐dimensional (2D) as well as in 3D using the DLP printer. In addition, the cytocompatibility of the printed porous scaffolds is investigated using fibroblasts, where high cell viabilities as well as cell proliferation, spreading, and migration are confirmed. It is anticipated that the strategy is widely applicable in a range of application areas such as tissue engineering and regenerative medicine, among others. 

Abstract Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning techniques such as embedding have been applied to reasoning and inference tasks such as query answering and knowledge base completion. These subsymbolic and learnable representations are well suited for handling noise and efficiency problems that plagued prior work. However, applying embedding techniques to spatial and temporal reasoning has received little attention to date. In this paper, we explore two research questions: (1) How do embedding-based methods perform empirically compared to traditional reasoning methods on QSR/QTR problems? (2) If the embedding-based methods are better, what causes this superiority? In order to answer these questions, we first propose a hyperbolic embedding model, called HyperQuaternionE, to capture varying properties of relations (such as symmetry and anti-symmetry), to learn inversion relations and relation compositions (i.e., composition tables), and to model hierarchical structures over entities induced by transitive relations. We conduct various experiments on two synthetic datasets to demonstrate the advantages of our proposed embedding-based method against existing embedding models as well as traditional reasoners with respect to entity inference and relation inference. Additionally, our qualitative analysis reveals that our method is able to learn conceptual neighborhoods implicitly. We conclude that the success of our method is attributed to its ability to model composition tables and learn conceptual neighbors, which are among the core building blocks of QSR/QTR. 

Abstract Narrative cartography is a discipline which studies the interwoven nature of stories and maps. However, conventional geovisualization techniques of narratives often encounter several prominent challenges, including the data acquisition & integration challenge and the semantic challenge. To tackle these challenges, in this paper, we propose the idea of narrative cartography with knowledge graphs (KGs). Firstly, to tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes to allow users to search and retrieve relevant data from integrated cross-domain knowledge graphs for narrative mapping from within a GISystem. With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping. Two use cases — Magellan’s expedition and World War II — are presented to show the effectiveness of this approach. In the meantime, several limitations are identified from this approach, such as data incompleteness, semantic incompatibility, and the semantic challenge in geovisualization. For the later two limitations, we propose a modular ontology for narrative cartography, which formalizes both the map content ( Map Content Module) and the geovisualization process ( Cartography Module). We demonstrate that, by representing both the map content and the geovisualization process in KGs (an ontology), we can realize both data reusability and map reproducibility for narrative cartography. 

Abstract. The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding its social drivers may be as important as understanding the virus itself. One such social driver is misinformation and distrust in institutions. This is particularly interesting as the scientific process is more transparent than ever before. Numerous scientific teams have published datasets that cover almost any imaginable aspects of COVID-19 during the last two years. However, consistently and efficiently integrating and making sense of these separate data “silos” to scientists, decision makers, journalists, and more importantly the general public remain a key challenge with important implications for transparency. Several types of knowledge graphs have been published to tackle this issue and to enable data crosswalks by providing rich contextual information. Interestingly, none of these graphs has focused on COVID-19 forecasts despite them acting as the underpinning for decision making. In this work we motivate the need for exposing forecasts as a knowledge graph, showcase queries that run against the graph, and geographically interlink forecasts with indicators of economic impacts.