Purpose The expression of eukaryotic translation initiation factor 2 subunit 3 (EIF2S3) in patients with non-small cell lung and colorectal cancer is lower than that in healthy individuals. However, the functions of EIF2S3 remain unclear, and its study in leukemia has not been reported. The article aims to explore the role of EIF2S3 in AML (acute myeloid leukemia) and its underlying mechanism.Methods Reverse transcription-quantitative PCR was performed to evaluate the expression levels of EIF2S3, and its association with patient prognosis was determined. Inducible HEL-EIF2S3 and HL-60-EIF2S3 cell lines were established by retrovirus infection. Cellular proliferation and the cell cycle were analyzed using Cell Counting Kit 8 and ow cytometric analyses. Tumorigenic ability was evaluated using xenograft nude mouse model. Gene expression pro les were analyzed in HL-60-EIF2S3 cells by next-generation sequencing, and WB analysis was performed to detect the expression of related proteins. ResultsThe expression of EIF2S3 in patients with AML was lower than that experiencing CR (P=0.02). Furthermore, EIF2S3 overexpression inhibited cellular proliferation, halted G0/1 to S phase cell cycle progression and inhibited tumorigenicity (P=0.015). 479 differentially expressed genes were identi ed between HL60-EIF2S3 DOX (-) and HL60-EIF2S3 DOX (+) cells via NGS and several of them involved in MAPK/ERK signaling pathway. The phosphorylation levels of ERK decreased when EIF2S3 was overexpressed(P 0.050).Conclusion EIF2S3 overexpression may result in a decrease in cellular proliferation, cell cycle arrest and tumorigenic inhibition via the MAPK/ERK signaling pathway in AML cells.

Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background is covered by various clutter and interference, leading to the serious obscuring of the target. To meet this challenge, a cross-correlation-based background subtraction (CCBS) method and an interference suppression technique are adopted in combination for more accurate detection. The CCBS method processes the raw echo by establishing a background-removal model and using the similarity between each A−Scan and a reference wave. In addition, a Butterworth filter is adopted to get rid of the active electromagnetic interference beyond the working frequencies of the system; then, a lateral Doppler filtering (LDF) technique is introduced to suppress the passive interference generated by the rotation of the UAV rotor itself. Moreover, a practical method for estimating the dielectric constant is introduced by the calibration process of the measured radar echo. Numerical simulations and experimental results by our UAV-GPR system demonstrate that the proposed method has presented a better performance than the traditional methods, and the system has great potential in detecting deeply buried targets.

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