The Meishan section, South China is the Global Stratotype Section and Point (GSSP) for the Permian-Triassic boundary (PTB), and also is well known for the best record demonstrating the Permian-Triassic mass extinction (PTME) all over the world. This section has also been studied using multidisciplinary approaches to reveal the possible causes for the greatest Phanerozoic biocrisis of life on Earth; many important scenarios interpreting the great dying have been proposed on the basis of data from Meishan. Nevertheless, debates on biotic extinction patterns and possible killers still continue. This paper reviews all fossil and sedimentary records from the Permo-Triassic (P-Tr) transition, based on previously published data and our newly obtained data from Meishan, and assesses ecologically the PTME and its aftermath to determine the biotic response to climatic and environmental extremes associated with the biocrisis. Eight updated conodont zones: C. yini, C. meishanensis, H. changxingensis, C. taylorae, H. parvus, I. staeschei, I. isarcica, and C. planate Zones are proposed for the PTB beds at Meishan.

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

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