To date, most EEG-based brain-computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test-retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.

The transcription factor E2F is an important modulator of the cell cycle, and the unrestricted activation of E2F-dependent transcription is considered to be an important driver of tumor formation and progression. E2F8 is known to play an important role in embryonic development and cell cycle control by inhibiting E2F1. However, it is not yet known whether E2F8 is involved in the progression of cervical cancer. In this study, the functional consequences of E2F8 knockdown in vitro and in vivo were explored. To demonstrate the function of E2F8 in cell proliferation, migration and invasion, we knocked down E2F8 in cervical cancer cell lines; in vitro and in vivo experiments using this knockdown showed that E2F8 potently induced the expression of epithelial-mesenchymal transition (EMT) markers. Finally, clinical data confirmed that E2F8 was a significant predictive factor for progression-free survival, and that patients with cervical cancer who exhibited high expression of E2F8 showed high FIGO stages and frequent recurrence rates compared to patients with low E2F8 expression. In conclusion, our study suggests that E2F8 is highly correlated with the progression-free survival of cervical cancer patients.

Background: Despite the recent research implicating E2F8 (E2F Transcription Factor 8) in cancer, the role of E2F8 in the progression of ovarian cancer has remained unclear. Hence, we explored the bio-functional effects of E2F8 knockdown on ovarian cancer cell lines in vitro and in vivo. Methods: The expression of E2F8 was compared between ovarian cancer and noncancer tissues, and its association with the progression-free survival of ovarian cancer patients was analyzed. To demonstrate the function of E2F8 in cell proliferation, migration, and invasion, we employed RNA interference to suppress E2F8 expression in ovarian cancer cell lines. Finally, the effect of E2F8 knockdown was investigated in a xenograft mouse model of ovarian cancer. Results: Ovarian cancer tissue exhibited significantly higher E2F8 expression compared to that of normal ovarian tissue. Clinical data showed that E2F8 was a significant predictor of progression-free survival. Moreover, the prognosis of the ovarian cancer patients with high E2F8 expression was poorer than that of the patients with low E2F8 expression. In vitro experiments using E2F8-knockdown ovarian cancer cell lines demonstrated that E2F8 knockdown inhibited cell proliferation, migration, and tumor invasion. Additionally, E2F8 was a potent inducer and modulator of the expression of epithelial–mesenchymal transition and Notch signaling pathway-related markers. We confirmed the function of E2F8 in vivo, signifying that E2F8 knockdown was significantly correlated with reduced tumor size and weight. Conclusions: Our findings indicate that E2F8 is highly correlated with ovarian cancer progression. Hence, E2F8 can be utilized as a prognostic marker and therapeutic target against ovarian malignancy.