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Background: Efforts to profile atopic dermatitis (AD) tissues have intensified, yet comprehensive analysis of systemic immune landscapes in severe AD remains crucial. Methods: Employing single‐cell RNA sequencing, we analyzed over 300,000 peripheral blood mononuclear cells from 12 severe AD patients (Eczema area and severity index (EASI) > 21) and six healthy controls. Results: Results revealed significant immune cell shifts in AD patients, including increased Th2 cell abundance, reduced NK cell clusters with compromised cytotoxicity, and correlated Type 2 innate lymphoid cell proportions with disease severity. Moreover, unique monocyte clusters reflecting activated innate immunity emerged in very severe AD (EASI > 30). While overall dendritic cells (DCs) counts decreased, a distinct Th2‐priming subset termed "Th2_DC" correlated strongly with disease severity, validated across skin tissue data, and flow cytometry with additional independent severe AD samples. Beyond the recognized role of Th2 adaptive immunity, our findings highlight significant innate immune cell alterations in severe AD, implicating their roles in disease pathogenesis and therapeutic potentials. Conclusion: Apart from the widely recognized role of Th2 adaptive immunity in AD pathogenesis, alterations in innate immune cells and impaired cytotoxic cells have also been observed in severe AD. The impact of these alterations on disease pathogenesis and the effectiveness of potential therapeutic targets requires further investigation. [ABSTRACT FROM AUTHOR]

In this work, we apply deep learning to estimate the direction-of-arrival (DoA) of multiple narrowband signals with a uniform linear array in a coherent environment. First, the logarithmic eigenvalue-based classification network (LogECNet) is introduced to enhance signal number detection accuracy in challenging scenarios, such as the low signal-to-noise (SNR) regime and limited snapshots. Next, a multi-label classification model called the root-spectrum network (RSNet) is devised to estimate the DoAs using the signal number inferred by LogECNet. In the proposed architecture, the full-row Toeplitz matrices reconstruction (FTMR), which exploits all rows of the signal covariance matrix (SCM), is combined with LogECNet and RSNet to inversely map the SCM to the numerical DoAs in the coherent scenario. It is shown that the eigenvalues factorized from the FTMR output matrix become more robust sources for signal enumeration than those of the forward/backward spatial smoothing (FBSS) algorithm. Furthermore, the logarithmic scaling of the eigenvalues of the FTMR results in LogECNet outperforming other detectors. The simulation results show our proposed method not only improves the signal number detection and angular estimation performance, but also achieves the complexity reduction with respect to the prior schemes. [ABSTRACT FROM AUTHOR]