The Natural Compound Myricetin Effectively Represses the Malignant Progression of Prostate Cancer by Inhibiting PIM1 and Disrupting the PIM1/CXCR4 Interaction

Background/Aims: Natural compounds are a promising resource for anti-tumor drugs. Myricetin, an abundant flavonoid found in the bark and leaves of bayberry, shows multiple promising anti-tumor functions in various cancers. Methods: The cytotoxic, pro-apoptotic, and anti-metastatic effects of myricetin on prostate cancer cells were investigated in both in vitro and in vivo studies. Short-hairpin RNA knockdown of the proviral integration site for Moloney murine leukemia virus-1 (PIM1), pull-down and co-immunoprecipitation assays, and an intracellular Ca2+ flux assay were used to investigate the potential underlying mechanism of myricetin. ONCOMINE database data mining and immunohistochemical analysis of prostate cancer tissues were used to evaluate the expression of PIM1 and CXCR4, as well as the correlation between PIM1 and CXCR4 expression and the clinicopathologic characteristics and prognoses of prostate cancer patients. Results: Myricetin exerted selective cytotoxic, pro-apoptotic, and anti-metastatic effects on prostate cancer cells by inhibiting PIM1 and disrupting the PIM1/CXCR4 interaction. Moreover, PIM1 and CXCR4 were coexpressed and associated with aggressive clinicopathologic traits and poor prognosis in prostate cancer patients. Conclusion: These results offer preclinical evidence for myricetin as a potential chemopreventive and therapeutic agent for precision medicine tailored to prostate cancer patients characterized by concomitant elevated expression of PIM1 and CXCR4

Fast Helmet and License Plate Detection Based on Lightweight YOLOv5

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations (FLOPs) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second (FPS) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy

Promoting surface reconstruction of NiFe layered double hydroxide for enhanced oxygen evolution

A dynamic surface reconstruction of oxide electrocatalysts in alkaline media is widely observed especially for layered double hydroxide (LDH), but little is known about how to promote the reconstruction toward desired surfaces for improved oxygen evolution reaction (OER). Here, surface reconstruction of NiFe LDH nanosheets is successfully induced to a higher degree via in situ sulfur doping than that by natural electrochemical activation. Theoretical calculations, operando Raman, and various ex situ characterizations reveal the S anion-induced effect can lower the energy barrier and facilitate the phase transformation into highly active S-doped oxyhydroxides. The generated S-NixFeyOOH can optimize the intermediate adsorption and facilitate the OER kinetics. The reconstructed S-oxyhydroxides catalyst presents superior OER activity and long-term durability compared to undoped ones. This work provides a structure–composition–activity relationship during the in situ surface restructuring of NiFe LDH pre-catalysts.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Submitted/Accepted versionThis research is financially supported by the National Natural Science Foundation of China (51872124), Agency for Science, Technology, and Research (A*STAR), Singapore by AME Individual Research Grants (A1983c0026), the Ministry of Education of China (6141A02022516), and the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG125/21). H.L. is thankful for the financial support from the China Scholarship Council (No.202106780011)

Practical Astronomy Education at the National University of Singapore (NUS)

10.1142/S2661339519500045The Physics Educator0101195000