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Programmed cell death (PCD) is a vital biological process that is essential for regulating cell progression and tumor microenvironment. This study aimed to explore the relationship between PCD-related genes expression and prognosis in thyroid cancer (THCA), especially IL20RA, as a potential prognostic marker for THCA. Data from The Cancer Genome Atlas (TCGA) database was utilized to develop a PCD-related risk prediction model based on LASSO regression along with univariate Cox regression. The correlation between PCD-related genes and immune cell infiltration was also assessed. The prognostic value of the key PCD-related gene for THCA was investigated by immunohistochemistry. The immune regulatory function and biological function of the key PCD related molecules were detected by cellular experiments. We identified four PCD-related genes (NPC2, E2F1, IL20RA, and TREM2) and constructed a risk model that exhibited excellent accuracy in predicting the prognosis of THCA. Moreover, we confirmed that high expression of IL20RA related to the poor prognosis of THCA. IL20RA promoted cell proliferation and IL20RA knockdown increased apoptosis and ferroptosis. Analysis of the immune microenvironment and detection of macrophages polarization showed that IL20RA promoted the polarization of M2 macrophages while reducing the polarization of M1. We constructed a prognostic prediction model and identified several PCD-related genes. The function of IL20RA which could potentially provide a foundation for additional investigations into diagnostic markers and treatment targets for THCA. [ABSTRACT FROM AUTHOR]

In the realm of radio frequency identification (RFID) systems, combatting counterfeit tags through anti-counterfeiting technologies has garnered significant attention, particularly physical layer identification methods, lauded for their cost-effectiveness and simplicity in deployment. Nonetheless, the performance of physical layer recognition method is significantly impacted by the conditions of the tag detection setting, especially in scenarios characterized by low signal-to-noise ratio (SNR), where classification accuracy tends to suffer. To tackle this challenge head-on, this study proposes the implementation of a cognitive risk control strategy, which fine-tunes tag distance within the tag classification process to bolster the SNR and enhance recognition precision. Beyond the enactment of cognitive risk control, this paper extends its focus to encompass enriched time domain and frequency domain feature extraction, totaling 104 features, aimed at further enhancing classification efficacy. Leveraging software-defined radio devices, classification experiments encompassing seven popular tag types from three distinct manufacturers were conducted. Results from these experiments reveal that upon integrating the cognitive risk control strategy, the average accuracy of tag classification experiences an approximate 11% increase. Concurrently, in comparison to traditional twenty-eight and seven features, the adoption of one-hundred-and-four features translates to an enhancement in classification accuracy by roughly 4.3% and 5.3%, respectively. These findings not only underscore the efficacy of cognitive risk control in elevating label classification accuracy within low SNR environments but also underscore the potential for augmenting classification performance through an increased feature set. [ABSTRACT FROM AUTHOR]