Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

Carbonate Ontology and Its Application for Integrating Microfacies Data

Carbonate rocks record essential information on changes in paleoclimate and paleoceanography. Abundant geological and geochemical data of carbonate rocks have been accumulated over the past decades;however, most of the data are stored in the published literature with highly unstructured forms, and are thus difficult to reuse. The ontology is a standard knowledge model for data integration, which can promote data storage and reutilization. This study proposes a carbonate ontology that represents the concepts in carbonate microfacies. The carbonate ontology constructed by the top-down process contains 215 terms of classifications and petrographic descriptions of carbonate rocks. Furthermore, carbonate microfacies of the Cretaceous(Aptian) carbonate platform in the Betic Cordillera and Jurassic carbonate platform in Tibet provide the data from case studies for the testing and initial validation of the proposed ontology. The carbonate ontology is under continuous expansion following the bottom-up approach and open access on the website of the deep-time digital Earth(DDE) program.

High-throughput“read-on-ski”automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids

Organoid models are used to study kidney physiology,such as the assessment of nephrotoxicity and underlying disease processes.Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies,but there is a need to accelerate basic and translational research in the field.Here,we developed an automated continuous imaging setup with the“read-on-ski”law of control to maximize temporal resolution with minimum culture plate vibration.High-accuracy performance was achieved:organoid screening and imaging were performed at a spatial resolution of 1.1µm for the entire multi-well plate under 3 min.We used the in-house developed multi-well spinning device and cisplatin-induced nephrotoxicity model to evaluate the toxicity in kidney organoids using this system.The acquired images were processed via machine learning-based classification and segmentation algorithms,and the toxicity in kidney organoids was determined with 95%accuracy.The results obtained by the automated“read-on-ski”imaging device,combined with label-free and non-invasive algorithms for detection,were verified using conventional biological procedures.Taking advantage of the close-to-in vivo-kidney organoid model,this new development opens the door for further application of scaled-up screening using organoids in basic research and drug discovery.