A Novel Extracellular Hsp90 Mediated Co-Receptor Function for LRP1 Regulates EphA2 Dependent Glioblastoma Cell Invasion

Extracellular Hsp90 protein (eHsp90) potentiates cancer cell motility and invasion through a poorly understood mechanism involving ligand mediated function with its cognate receptor LRP1. Glioblastoma multiforme (GBM) represents one of the most aggressive and lethal brain cancers. The receptor tyrosine kinase EphA2 is overexpressed in the majority of GBM specimens and is a critical mediator of GBM invasiveness through its AKT dependent activation of EphA2 at S897 (P-EphA2(S897)). We explored whether eHsp90 may confer invasive properties to GBM via regulation of EphA2 mediated signaling.We find that eHsp90 signaling is essential for sustaining AKT activation, P-EphA2(S897), lamellipodia formation, and concomitant GBM cell motility and invasion. Furthermore, eHsp90 promotes the recruitment of LRP1 to EphA2 in an AKT dependent manner. A finding supported by biochemical methodology and the dual expression of LRP1 and P-EphA2(S897) in primary and recurrent GBM tumor specimens. Moreover, hypoxia mediated facilitation of GBM motility and invasion is dependent upon eHsp90-LRP1 signaling. Hypoxia dramatically elevated surface expression of both eHsp90 and LRP1, concomitant with eHsp90 dependent activation of src, AKT, and EphA2.We herein demonstrate a novel crosstalk mechanism involving eHsp90-LRP1 dependent regulation of EphA2 function. We highlight a dual role for eHsp90 in transducing signaling via LRP1, and in facilitating LRP1 co-receptor function for EphA2. Taken together, our results demonstrate activation of the eHsp90-LRP1 signaling axis as an obligate step in the initiation and maintenance of AKT signaling and EphA2 activation, thereby implicating this pathway as an integral component contributing to the aggressive nature of GBM

Inversion of Rayleigh Wave Dispersion Curves via Long Short-Term Memory Combined with Particle Swarm Optimization

An essential step in surface wave exploration is the inversion of dispersion curves. By inverting dispersion curves, we can effectively establish the shear-wave velocity model and obtain reliable subsurface stratigraphic information. The inversion of dispersion curves is an inversion problem with multiple parameters and multiple poles, and obtaining a high precision solution is difficult. Among the methods of inversion of dispersion curves, local search methods are prone to fall into local extremes, and global search methods such as particle swarm optimization (PSO) and genetic algorithm (GA) present the disadvantages of slow convergence speed and low precision. Deep learning models with strong nonlinear mapping capability can effectively solve nonlinear problems. Therefore, we propose a method called PSO-optimized long short-term memory (LSTM) network (PSO-LSTM) to invert the dispersion curves in order to improve the effect of inversion of dispersion curves. The method is based on the LSTM network, and PSO is used to optimize the LSTM network structure and other parameters that need to be given manually to improve the prediction of the network. Two theoretical geological models are used in the paper: Model A and Model B to test the PSO-LSTM. The tests include the noisy data test and noise-free data test. Model A was tested without noise, and Model B was tested with noise. In addition, PSO and LSTM were tested on model A to compare the performance of PSO-LSTM. In Model A, the maximum relative errors of PSO and LSTM are 20.76% and 5.85%, respectively, and the maximum standard deviations of PSO and LSTM are 57.37 and 1.97, respectively. For PSO-LSTM, the maximum relative errors of Model A and Model B in the inverse results are 2.05% and 2.09%, and the maximum standard deviations of Model A and Model B in the inverse results are 1.23 and 3.87, respectively. The test results of Model A show that the inversion performance of PSO-LSTM is better than those of LSTM and PSO, and the performance of the network can be improved after PSO is used to optimize the network parameters. The inverse results from Model B show that the PSO-LSTM is robust and can invert the dispersion curves well even after adding noise to the model. Finally, the PSO-LSTM is used to invert the actual data from Wyoming, USA, which demonstrates that the PSO-LSTM can be used for the quantitative interpretation of Rayleigh wave dispersion curves