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Background: Breast cancer is the most common malignancy in women worldwide, so it is imperative to find new medications for this disease. The dry seeds of Brucea javanica have been extensively used in Traditional Chinese Medicine (TCM) for the treatment of tumor. Objectives: To isolate antitumor peptides from B. javanica seeds and research their antitumor activity with MCF-7 cells. Materials and Methods: Globulin of B. javanica was extracted and enzymatically hydrolyzed by pepsin. Then ultrafiltration, gel filtration chromatography (GFC), and reverse-phase high-performance liquid chromatography (RP-HPLC) were employed to purify the peptides. Anticancer activity was studied in humans in human breast cancer MCF-7 cells, MTT (3- [4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide)-assessed cell viability, Giemmsa staining determined whether the cells should undergo apoptosis, and apoptosis and cycle assays assessed the antitumor mechanism of the cells. Real time quantitative polymerase chain reaction (RT-qPCR) was used to study the alterations of oncogenes and cancer metastasis suppressor genes in MCF-7 cells treated with peptide extracts. Results: The fraction F9-9 extracted from B. javanica significantly inhibited the proliferation of MCF-7 cells with the IC50 of 0.124 ± 0.004 μg/mL. The possible mechanisms of this fraction were further explored. F9-9 obviously induced cell cycle arrest at G0/G1 phase and promoted apoptosis of MCF-7 cells. The underlying molecular mechanisms were explored by RT-qPCR and results showed that in B. javanica small molecular peptides upregulated the expression of tumor suppressor genes P53 and PTEN as well as tumor metastasis suppressor gene NM23H-1. Conclusion: This study suggests that B. javanica peptides have therapeutic activity and mechanisms for antibreast cancer, encouraging further isolation and purification to obtain monomeric peptides of this extract for anticancer use. [ABSTRACT FROM AUTHOR]

Remote sensing image target detection is widely used for both civil and military purposes. However, two factors need to be considered for remote sensing image target detection: real-time and accuracy for detecting targets that occupy few pixels. Considering the two above issues, the main research objective of this paper is to improve the performance of the YOLO algorithm in remote sensing image target detection. The reason is that the YOLO models can guarantee both detection speed and accuracy. More specifically, the YOLOv3 model with an auxiliary network is further improved in this paper. Our model improvement consists of four main components. Firstly, an image blocking module is used to feed fixed size images to the YOLOv3 network; secondly, to speed up the training of YOLOv3, DIoU is used, which can speed up the convergence and increase the training speed; thirdly, the Convolutional Block Attention Module (CBAM) is used to connect the auxiliary network to the backbone network, making it easier for the network to notice specific features so that some key information is not easily lost during the training of the network; and finally, the adaptive feature fusion (ASFF) method is applied to our network model with the aim of improving the detection speed by reducing the inference overhead. The experiments on the DOTA dataset were conducted to validate the effectiveness of our model on the DOTA dataset. Our model can achieve satisfactory detection performance on remote sensing images, and our model performs significantly better than the unimproved YOLOv3 model with an auxiliary network. The experimental results show that the mAP of the optimised network model is 5.36% higher than that of the original YOLOv3 model with the auxiliary network, and the detection frame rate was also increased by 3.07 FPS. [ABSTRACT FROM AUTHOR]