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This research explores the intricate-coupling relationship between typhoons and the ionosphere by the high-frequency surface wave radar's (HFSWR) over-the-horizon capabilities. This letter describes the gravity wave features in travelling ionospheric disturbances (TIDs) and ionospheric drift information. Further discussion is raised on the chaotic properties of gravity wave features using the Lyapunov exponents and chaotic attractors, particularly as they are influenced by typhoon. To predict these gravity wave features more accurately, a new model is developed by synergizing phase space reconstruction theory and statistical learning theory, a chaotic prediction model for gravity wave features constructed on a third-order Volterra filter. The experimental findings confirm that the gravity waves features detected via HFSWR present the hyperchaotic behaviour. More importantly, the innovative gravity wave prediction model based on chaotic attractor can capture the dynamics of the original chaotic system. Notably, the model's prediction accuracy has been improved by 98.6% after the introduction of phase space reconstruction modelling. [ABSTRACT FROM AUTHOR]

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the "ConsensusClusterPlus" package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the "glmnet" package in R, while the random forest algorithm was realized using the "randomForest" package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the "pRRophetic" package in R and the Genomics of Drug Sensitivity in Cancer database. The "rms" package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed. [ABSTRACT FROM AUTHOR]

The ionosphere is an integral element of the Earth and reflects the variations of the Earth's space weather and solar activity. Since extreme weather can cause ionospheric disturbances, changes in the ionosphere can indirectly enable early warning of extreme weather. The major intention of predicting the peak height of the ionospheric F2 layer (hmF2) in this paper is to acquire ionospheric variations over a period of time in a local area to facilitate future extreme weather warning research. In this paper, a dual element LSTM-CNN (long short term memory-convolutional neural network) prediction model is proposed to predict the hmF2. The performance of the proposed model is assessed by comparing it with other popular models such as SARIMA (seasonal differential autoregressive moving average), LSTM (long short term memory), BP (back propagation neural network) and IRI2016 (international reference ionospheric model) models. The outcome demonstrates that the prediction effect with the proposed model is remarkably excellent in comparison with the remaining four models. Furthermore, the proposed model has better sensitivity to rapid changes in parameters. The outcomes indicate that the forecasting model of this study has high prediction capabilities. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a worldwide public health concern. We aimed to investigate the association between preoperative prognostic scoring system based on the combination of age, American Society of Anesthesiologists physical status (ASA-PS), and prognostic nutritional index (PNI) and long-term survival outcomes in patients with (GC). Data from 513 patients were analyzed using Cox proportional hazards regression models to evaluate the association between this prognostic score system and risks of all-cause mortality. This simple prognostic score system (0–3 points) was an independent predictor of long-term survival outcomes in patients with GC after radical gastrectomy based on multivariate analyses. Prognostic 1-point score, 2-point score, and 3-point score significantly increased 50% (95% CI, 14%–98%; P = 0.004), 75% (95% CI, 22%–151%; P = 0.003), and 116% (95% CI, 26%–271%; P = 0.005) hazards of 5-year all-cause mortality, respectively, compared to prognostic 0-point score. Five-year overall survival rates were significantly decreased as prognostic scores increased, (0 point, 57.3%; 1-point, 41.3%; 2-ponint, 36.6%; 3-point, 25.9%; P < 0.01; area under the curve [AUC] = 0.62). A prognostic scoring method based on combination of age, ASA-PS, and PNI may serve as an independent risk stratification metric for long-term survival in patients with GC. [ABSTRACT FROM AUTHOR]