Dynamic time history analysis is a powerful and effective method to study the seismic response of tunnels, the dynamic time history to analyze the seismic response of the tunnel in the typical subway in this paper. The analysis results show that the overall levelling of the tunnel will not affect the tunnel too much, and the seismic response of the tunnel is mainly related to the relative displacement of the ground around the tunnel. The analysis results show that the internal force of the tunnel and the tunnel inclination have a good linear relationship, and the tunnel inclination can be used to measure the magnitude of the seismic response of the tunnel.

Introduction: This study aims to characterize changes in biogenic amines patterns in glioblasto-ma patients undergoing surgery and concurrent chemoradiation to illustrate how this class of metabolites changes during different stages of standard care treatment protocol. Methods: We examined 138 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, immediately after chemoradiation, and after adju-vant chemotherapy treatment) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics of biogenic amines was used given its superiority for identifying and quantitating small metabolites; this yielded 340 structurally identified metabolites. Results: Comparing post-surgery to pre-surgery showed increased level of 12 metabolites: Gly-codeoxycholic acid (P=7.79E-05), Betonicine (P=9.23E-05), Glycocholic acid (P=3.18E-03), 3-Cysteinylaectaminophen (P=4.95E-03), S-Methyl-3-thioacetaminophen (P=5.77E-03), Tau-rocholic acid (P=9.97E-03), N-glycine (P=0.0013), p-acetamidophenyl.beta.-D-glucuronide (P=0.0048), 2-Hydroxy-5-sulfopyridine-3-carboxylic acid (P=0.0074), Acetaminophen sulfate (P=0.0095), 2-Amino-3-methoxybenzoic acid (P=0.02), and Dehydrofelodipine (P=0.024). There were 11 compounds that were downregulated, which included Mannitol (P=1.96E-19), Sorbitol (P=2.75E-019), Linoleic acid (P=1.51E-10), 1-Methylnicotinamide (P=1.78E-09), Nudifloramide (P=2.81E-08), Hexadecanedioic acid (P=5.49E-05), 3-Hydroxybutyric acid (P=0.0002), Riluzole (0.0036), Bupivacaine (P=0.0041), Lactitol (P=0.008), and 1-hydroxymidazolam.beta.-D-glucuronide (P=0.0414). After chemoradiation, significant de-crease was uncovered in Famotidine (P=0.0074), N-Isovalerylglycine (P=0.012), and 3-Methylcrotonylglycine (P=0.0131). While significant increase was detected in N-Methylisoleucine (P=0.0011), 4-Methyl-5-thiazoleethanol (P=0.042), and 6-Hydroxycaproic acid (P=0.049). Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). One significant insight gleamed from these models were that the metabolites Sorbitol and N-methylisoleucine were identified as important predictive features and confirmed by SHAP analysis. Conclusion: To our knowledge, this is the first study to describe plasma biogenic amines signa-tures during different treatment phases in patients with glioblastoma. A larger study is needed to confirm the results and the potential application of this algorithm for classification of treatment responses.

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.