Eight marine transgressions have been recognized from more than 30 deep drill holes (ca. 300 m deep) through Quaternary sequences in the Yangtze River delta region. These are, in ascending order, the Rugao and Zhoupe transgressions (early Pleistocene); the Shanghai, Jiading, and Wangdian transgressions in the middle Pleistocene; the Jiangyin and Gehu transgressions in the late Pleistocene; and the Zhenjiang transgression in the Holocene. The transgressions correspond to warm periods and regressions to cold periods. The younger transgressions were not only of shorter duartion, but also of larger magnitude. The findings verify that there were five moderately warm periods during the early to middle Pleistocene and two very warm periods during the late Pleistocene in the Yangtze River delta region.
Background
Light microscopy to study the infection of fungi in skin specimens is time‐consuming and requires automation.
Objective
We aimed to design and explore the application of an automated microscope for fungal detection in skin specimens.
Methods
An automated microscope was designed, and a deep learning model was selected. Skin, nail and hair samples were collected. The sensitivity and the specificity of the automated microscope for fungal detection were calculated by taking the results of human inspectors as the gold standard.
Results
An automated microscope was built, and an image processing model based on the ResNet‐50 was trained. A total of 292 samples were collected including 236 skin samples, 50 nail samples and six hair samples. The sensitivities of the automated microscope for fungal detection in skin, nails and hair were 99.5%, 95.2% and 60%, respectively, and the specificities were 91.4%, 100% and 100%, respectively.
Conclusion
The automated microscope we developed is as skilful as human inspectors for fungal detection in skin and nail samples; however, its performance in hair samples needs to be improved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.