ABSTRACT
The inadequate stability of organic-inorganic hybrid perovskites remains a significant barrier to their widespread commercial application in optoelectronic devices. Aging phenomena profoundly affect the optoelectronic performance of perovskite-based devices. In addition to enhancing perovskite stability, the real-time detection of aging status, aimed at monitoring the aging progression, holds paramount importance for both fundamental research and the commercialization of organic-inorganic hybrid perovskites. In this study, the aging status of perovskite was real-time investigated by using terahertz time-domain spectroscopy. Our analysis consistently revealed a gradual decline in the intensity of the absorption peak at 0.968 THz with increasing perovskite aging. Furthermore, a systematic discussion was conducted on the variations in intensity and position of the terahertz absorption peaks as the perovskite aged. These findings facilitate the real-time assessment of perovskite aging, providing a promising method to expedite the commercialization of perovskite-based optoelectronic devices.
ABSTRACT
Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)). We used unsupervised clustering to divide the classification subproblems and screened differentially methylated sites using Analysis of variance (ANOVA) test, Tukey-kramer test, and Boruta algorithms to construct models for each classifier module. After validation, CHCT accurately classified 1568 out of 1660 cases in the test set, with an average accuracy of 94.46%. We further curated an independent validation cohort of 677 cancer samples from GEO and assigned a diagnosis using CHCT, which showed high diagnostic potential with generally high accuracies (an average accuracy of 91.40%). Moreover, CHCT demonstrates predictive capability for additional cancer types beyond its original classifier scope as demonstrated in the medulloblastoma and pituitary tumor datasets. In summary, CHCT can hierarchically classify primary cancer by methylation profile, by splitting a large-scale classification of 30 cancer types into ten smaller classification problems. These results indicate that cancer hierarchical classification has the potential to be an accurate and robust cancer classification method.