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Rapid and accurate earthquake magnitude estimations are essential for earthquake early warning (EEW) systems. The distance information between the seismometers and the earthquake hypocenter can be important to the magnitude estimation. We designed a deep-learning, multiple-seismometer-based magnitude estimation method using three heterogeneous multimodalities: three-component acceleration seismograms, differential P-arrivals, and differential seismometer locations, with a specific transformer architecture to introduce the implicit distance information. Using a data-augmentation strategy, we trained and selected the model using 5365 and 728 earthquakes. To evaluate the magnitude estimation performance, we use the root mean square error (RMSE), mean absolute error (MAE), and standard deviation error (ϭ) between the catalog and the predicted magnitude using the 2051 earthquakes. The model could achieve RMSE, MAE, and ϭ less than 0.38, 0.29, and 0.38 when the passing time of the earliest P-arrival is 3 s and stabilize to the final values of 0.20, 0.15, and 0.20 after 14 s. The comparison between the proposed model and model ii, which is retrained without the specific architecture, indicates that the architecture contributes to the magnitude estimation. The P-arrivals picking error testing indicates the model could provide robust magnitude estimation on EEW with an absolute error of less than 0.2 s. [ABSTRACT FROM AUTHOR]

On-site earthquake early warning systems use the information from the initial P-wave to quickly estimate the degree of local damage. Existing on-site earthquake early warning models focus on ground motion information and generally ignore the degree of structural response, with many machine learning models characterized by poor interpretability. Therefore, an on-site prediction model based on XGBoost with a continuous time window and multi-feature inputs is proposed. The model uses the response spectrum value, which is closely related to the structural response, as the predicted intensity measure and uses strong motion data from Japanese K-NET stations for training. After using permutation importance to eliminate irrelevant input feature parameters, 11 parameters (P a , P d, P av , P ad , IAV, IV2, I a , TP, F max , TW, T) are determined as the final model input parameters. The results based on a test dataset show that 3 s after P-wave arrival, the mean square error of the model prediction is 15.10*10−4 g. As the input time window is extended, the mean square error of the model prediction gradually decreases, and the overestimation of small values is mitigated. Additionally, 10 s after P-wave arrival, the mean square error decreases by 7.66*10−4 g compared with that at 1 s. After using the PDP and SHAP methods to explain the model, T, I a , and F max were determined as the feature parameters with the greatest impact on the model results. Generalization tests base on large seismic events not included in the training and test datasets indicated that the model has good generalization capabilities. • An on-site response spectra prediction method based on XGBoost with a continuous time window and multi-feature inputs is proposed. • The proposed method eliminates irrelevant input feature parameters through permutation importance. • The proposed method can be interpreted using SHAP method. [ABSTRACT FROM AUTHOR]

Background: The mechanisms through which Mycobacterium tuberculosis evades immune surveillance during tuberculosis (TB) infection remain complex. Previous studies have found that Mycobacteria can manipulate the miRNAs of host cells to promote their survival during host-pathogen interactions, and most of these effects occur at the cellular miRNA level. We attempted to investigate the possible related mechanisms at the exosomal miRNA level. Results: High-throughput sequencing revealed that Bacillus Calmette-Guérin (BCG) infection could alter the composition of the macrophage exosome content, and the expression levels of miRNAs in exosomes derived from the cell culture media of macrophages showed significant differences between the BCG-infected and non-infected groups. Compared with the non-infected group, 20 exosomal miRNAs were up-regulated and 7 exosomal miRNAs were down-regulated in the infection group (p < 0.05), of which mmu-miR-27b-3p, mmu-miR-93-5p, mmu-miR-25-3p, mmu-miR-1198-5p, mmu-let-7c-5p and let-7a-5p were significantly up-regulated. A bioinformatic analysis indicated that these differentially expressed exosomal miRNAs were involved in multiple biological processes and pathways. The target genes of top six miRNAs in up-regulated groups were positively correlated with the regulation of apoptosis. Conclusions: The expression profile of miRNA in exosomes derived from macrophage were altered after Mycobacterium Bovis Bacillus Calmette-Guérin infection, and the differentially expressed miRNAs were involved in multiple biological processes and signalling pathways. The top six up-regulated miRNAs and their targeted genes were predominantly correlated with the regulation of apoptosis. [ABSTRACT FROM AUTHOR]