We developed a murine spine metastasis model by screening five metastatic non-small cell lung cancer cell lines (PC-9, A549, NCI-H1299, NCI-H460, H2030). A549 cells displayed the highest tendency towards spine metastases. After three rounds of selection in vivo, we isolated a clone named A549L6, which induced spine metastasis in 80% of injected mice. The parameters of the A549L6 cell spinal metastatic mouse models were consistent with clinical spine metastasis features. All the spinal metastatic mice developed symptoms of nerve compression after 40 days. A549L6 cells had increased migration, invasiveness and decreased adhesion compared to the original A549L0 cells. In contrast, there was no significant differences in cell proliferation, apoptosis and sensitivity to chemotherapeutic agents such as cisplatin. Comparative transcriptomic analysis and Real-time PCR analysis showed that expression of signaling molecules regulating several tumor properties including migration (MYL9), metastasis (CEACAM6, VEGFC, CX3CL1, CST1, CCL5, S100A9, IGF1, NOTCH3), adhesion (FN1, CEACAM1) and inflammation (TRAF2, NFκB2 and RelB) were altered in A549L6 cells. We suggest that migration, adhesion and inflammation related genes contribute to spine metastatic capacity.
Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.
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