Objective:To explore the differences of wear resistance of three kinds of glass ceramics and Wieland Zenostar zircona (Zenostar) , and to clarify their influencing factors.Methods:Zenostar were made into flat-shaped specimens (zirconia base sample group) and hemisphere-shaped specimens (zirconia pair grinding group) .There kinds of glass ceramics IPS Empress (Empress) , IPS e.max CAD (e.max) , VITA Suprinity (Suprinty) were used as base specimens.Each group was exposed to UMT-2testing machine to simulate the clinical service.The wear depthes of base specimens were detected by laser confocal scanning.Scanning electron microscope (SEM) was used to evaluate the wear surfaces.Results:In zirconia base sample group, there were no significant differences in the maximum wear depthes to Zenostar between the three kinds of glass ceramics (P＞0.05) .In zirconia pair grinding group, the maximum wear depthes ranked as follows:Zenostar group＜e.max group≈Empress group＜Suprinity group;there was no significant difference between e.max group and Empress group (P＞0.05) , but there were significant differences between other groups (P＜0.01) .The SEM results showed that the wearing surface of the Zenostar in zirconia base sample group was relatively smooth;whereas the wearing surface of Empress in zirconia pair grinding group was rougher with alarge area of clebris desquamation surface.Conclusion:The wear resistance of the three kinds of glass ceramics to Zenostar is related to the compositions and the chemical structures of materials.

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypi-cal information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcrip-tomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in dif-ferent biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.