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The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is vital to predict NAO events. Since the NAO event can be quantified by the NAO index, an effective neural network model EEMD-ConvLSTM, which is based on Convolutional Long Short-Term Memory (ConvLSTM) with Ensemble Empirical Mode Decomposition (EEMD), is proposed for NAO index prediction in this paper. EEMD is applied to preprocess NAO index data, which are issued by the National Oceanic and Atmospheric Administration (NOAA), and NAO index data are decomposed into several Intrinsic Mode Functions (IMFs). After being filtered by the energy threshold, the remaining IMFs are used to reconstruct new NAO index data as the input of ConvLSTM. In order to evaluate the performance of EEMD-ConvLSTM, six methods were selected as the benchmark, which included traditional models, machine learning algorithms, and other deep neural networks. Furthermore, we forecast the NAO index with EEMD-ConvLSTM and the Rolling Forecast (RF) and compared the results with those of Global Forecast System (GFS) and the averaging of 11 Medium Range Forecast (MRF) model ensemble members (ENSM) provided by the NOAA Climate Prediction Center. The experimental results show that EEMD-ConvLSTM not only has the highest reliability from evaluation metrics, but also can better capture the variation trend of the NAO index data.

Background: Epilepsy is one of the most common neurological disorders, but its underlying mechanism has remained obscure, and the role of immune-related genes (IRGs) in epilepsy have not yet been investigated.Therefore, in this study, we explored the association between IRGs and epilepsy.Methods: An IRG list was collected from the ImmPort database. The gene expression profiles of GSE143272 were collected from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm. nih.gov/geo/). Differentially expressed genes (DEGs) between epilepsy and normal samples were analyzed, and the intersections between IRGs and DEGs were identified using the VennDiagram package, with the intersected genes subjected to further analysis. Enrichment function for intersected genes were performed, constructed a protein-protein interaction (PPI) network via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes (top 10) of the PPI network were calculated by the cytoHubba plug-in in Cytoscape. The top correlated genes were selected to perform correlation analysis with immune cells infiltration and expression levels. Finally, we performed validation of the top correlated genes transcriptional expression levels using an animal model.Results: There were a total of 245 DEGs detected in GSE143272, among which 143 were upregulated and 102 downregulated genes in epilepsy. A total of 44 differential IRGs were obtained via intersection of DEGs and IRGs. Enrichment function analysis of DEGs showed that they played a significant role in immune response. The gene CXCL1 was the most correlated with other differentially expressed IRGs via the PPI network. The results of immune cell infiltration analysis indicated that epilepsy patients had higher activated mast cells infiltration (P=0.021), but lower activated CD4 memory T cells (P=0.001), resting CD4 memory T cells (P=0.011), and gamma delta T cells (P=0.038) infiltration. It was revealed that CXCL1 and activated mast cells (R=0.25, P=0.019) and neutrophils (R=0.3, P=0.0043), and a negative correlation with T cells gamma delta (R=−0.25, P=0.018). The levels of CXCL1 expression were significantly lower in epilepsy patients than those in normal samples.Conclusions: In this study, the results showed that IRGs such as CXCL1 have a significant influence on epilepsy via regulation of immune cells infiltration.

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