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Purpose: This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis. Methods: SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses. Results: Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets. Conclusion: GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

The promotion of industrial-mode production of Codonopsis pilosula Nannf. var. modesta (Nannf.) L. T. Shen (C. pilosula) has expanded the demand for the postharvest storage of fresh roots. Further research is needed to establish comprehensive methods to evaluate the impact of storage conditions. This study simulated the storage process of roots at near-freezing temperature [NFT (−1 °C)] and traditional low temperatures (−6 °C, 4 °C and 9 °C) for 40 days. At different storage stages, correlation analysis was conducted using quantitative data on 20 parameters, including sensory quality, active components, antioxidant capacity and physiological changes. Appearance and principal component analysis could distinguish between fresh and stored samples, while NFT samples on the 40th day of storage were similar to fresh ones. Correlation analysis indicated that NFT storage could maintain the sensory quality by increasing the antioxidant enzyme activity and active components, reducing the accumulation of reactive oxygen species and malondialdehyde and reducing the activity of browning-related enzymes and cell-wall-degrading enzymes. These findings highlight the importance of the overall quality evaluation of fresh roots and emphasize the potential to improve fresh root and dried medicinal material quality by regulating storage conditions such as temperature. [ABSTRACT FROM AUTHOR]

Surface Electromyography (sEMG) is widely used in evaluating the functional status of hands to assist in hand gesture recognition in many fields of treatment and rehabilitation. Multi-channel parallel interfaces (MCPIs) or time-division multiple access (TDMA) interfaces are the main technologies for the man–machine communication medium of sEMG recognition instruments. However, they can also result in a complex circuit connection and noise interference. A hand gesture recognition model based on sEMG signals by using single-mixture source separation and flexible neural trees (FNTs) is a breakthrough model of hand gesture recognition designed to conquer the above defects. It distinguishes itself from the traditional MCPI or TDMA interfaces by more accurate and reliable measurements of signals. Single-mixture source separation by use of ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and independent component analysis (ICA) is a novel single-input multiple-output (SIMO) blind separation method, which can simplify the two interfaces described above. The FNT model is generated and evolved based on the pre-defined simple instruction sets, which can solve the highly structure dependent problem of the artificial neural network. The testing has been conducted using several experiments conducted with five participants. The EEMD-PCA-ICA algorithm can blind separate single mixed signals with higher cross-correlation and lower relative root mean squared error. The results indicate that the model is able to classify four different hand gestures up to 97.48% accuracy. [ABSTRACT FROM PUBLISHER]

Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends' extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE). [ABSTRACT FROM AUTHOR]

Blind source separation of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Numerous blind source separation methods are commonly used for blind separation of single input multiple output. However, the priori knowledge of the signal is assumed to be known or the main channels selected from multi-channel output are not self-adaptive and automatic. Presented in this paper is a new method based on dimensionality reduction of ensemble empirical mode decomposition (EEMD), and ICA does not rely on such assumptions. The EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction IMFs. Principal component analysis (PCA) is applied to reduce dimensions of IMFs. The separated performance of EEMD-PCA-ICA algorithm is compared with EEMD-ICA through simulations, and experimental results show EEMD-PCA-ICA algorithm outperforms EEMD-ICA with higher cross-correlation and lower relative root mean squared error (RRMSE). [ABSTRACT FROM AUTHOR]