Inner cell mass (ICM) cells were isolated immunosurgically from day 7-8 horse blastocysts and, after proliferation in vitro for 15-28 passages, three lines of cells were confirmed to be embryonic stem (ES) cells by their continued expression of alkaline phosphatase activity and their ability to bind antisera specific for the recognized stem cell markers, SSEA-1, TRA-1-60, TRA-1-81, and the key embryonic gene Oct-4. When maintained under feeder cell-free conditions in vitro, the three lines of cells differentiated into cells of ectodermal, endodermal, and mesodermal lineages. However, they did not form teratomata when injected into the testes of severe combined immunodeficiency (SCID)/beige immunoincompetent mice, thereby indicating a significant difference in phenotype between ES cells of the horse and those of the mouse and human.
As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.
In practice, early detection of disease is of high importance to practical value, corresponding measures can be taken at the early stage of plant disease. However, in the early stage of disease or when a rare disease occurs, there are limited training samples in practice, which makes machine learning especially deep learning models hardly work well while the stronger representative ability needs large-scale training data. To solve this problem, a fine grained-GAN based grape leaf spot identification method was proposed for local spot area image data augmentation to the generated local spot area images which were added and fed them into deep learning models for training to further strengthen the generalization ability of the classification models, which can effectively improve the accuracy and robustness of the prediction. Including 500 early-stage grape leaf spot images every category were fed into the proposed fine grained-GAN for local spot area data augmentation, 1000 local spot area sub images every category were generated in this study. The improved faster R-CNN was integrated in fine grained-GAN as local spot area detector. After that, the segmented sub-images and generated sub-images were mixed as training data input into the deep learning models, while the original segmented sub-images were used for testing. Experimental results showed that the proposed method had achieved higher identification accuracy on five state-of-art deep learning models; especially ResNet-50 had got 96.27% accuracy, which obtained significant improvements than other data augmentation methods and verified its satisfactory performance. This is of great practical significance for rare diseases or limited training samples.INDEX TERMS Fine grained-GAN, grape leaf spot identification, deep learning, few-shot learning, agricultural engineering.
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