The whole plants of Ajuga lupulina afforded five compounds, including three new clerodane diterpenes, lupulins A-C (1-3), whose structures were elucidated by spectral methods. Among these compounds, lupulins A (1) and B (2) as well as the acid hydrolysate (5) of lupulin D (4) showed antibacterial activities against Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli.

In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

We performed ab initio molecular dynamics simulations to investigate the initiation mechanisms and subsequent decompositions of a 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) crystal at initial decomposition temperature coupled with different pressures. The initial decomposition step of TATB was found to be the unimolecular intramolecular hydrogen transfer; moreover, this initiation mechanism is independent of the variation of the pressure.

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