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Objective: Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. Methods: A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. Results: The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%. [ABSTRACT FROM AUTHOR]

Introduction: The prognostic capability of targeted sequencing of primary tumors in patients with estrogen receptor‐positive, human epidermal growth factor receptor‐2‐negative early‐stage invasive breast cancer (EBC) in a real‐world setting is uncertain. Therefore, we aimed to determine the correlation between a 22‐gene mutational profile and long‐term survival outcomes in patients with ER+/ERBB2‐ EBC. Patients and Methods: A total of 73 women diagnosed with ER+/ERBB2‐ EBC between January 10, 2004, and June 2, 2008, were followed up until December 31, 2022. Univariate and multivariate Cox models were constructed to plot the relapse‐free survival (RFS) and overall survival (OS). The log‐rank test derived p‐value was obtained. For external validation, we performed a survival analysis of 1163 comparable patients retrieved from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Results: At follow‐up, 16 (21.9%) patients had relapsed, while 21 (nearly 29%) harbored mutant genes. Thirty‐three missense mutations were detected in 14 genes. The median ages were 51 and 46 years in patients with and without mutations, respectively. Patients with any mutation had a 1.85‐fold higher risk of relapse (hazard ratio [HR]: 1.85, 95% confidence interval [CI]: 0.60–5.69) compared to those without any mutation. Patients who harbored any of the six genes (MAP2K4, FGFR3, APC, KIT, RB1, and PTEN) had a nearly 6‐fold increase in the risk of relapse (HR: 5.82, 95% CI: 1.31–18.56; p = 0.0069). Multivariate Cox models revealed that the adjusted HR for RFS and OS were 6.67 (95% CI: 1.32–27.57) and 8.31 (p = 0.0443), respectively. METABRIC analysis also demonstrated a trend to significantly worse RFS (p = 0.0576) in the subcohort grouped by having a mutation in any of the six genes. Conclusions: Our single‐institution tissue bank study of Taiwanese women with ER+/ERBB2‐ EBC suggests that a novel combination of six gene mutations might have prognostic capability for survival outcomes. We studied the prognosis of a 22‐gene mutational profile using tissue bank tumors of 73 women with ER+/ERBB2‐ EBC. This cohort of female patients had extended follow‐up, reaching 18.8 years. Any mutation in the MAP2K4, FGFR3, APC, KIT, RB1, and PTEN genes would have a 5.8‐fold increased risk of relapse. [ABSTRACT FROM AUTHOR]

Regional higher education growth in non-federal states has not attracted much academic attention. This paper is one of the first attempts to explore China's latest higher education expansion and its systematic and regional impact from the perspective of multi-level governance. This article argues that the state had explicitly utilized the Commanding Heights Strategy to diffuse the higher education authority to sub-national level and promote regional growth. The Central authorities allowed the Ministry of Education establishing a vertical elite coalition to command the heights of tertiary institutional hierarchy and key resources for tertiary development. In addition, the state used both financial incentives and sectoral incentives to mobilize resources for regional expansion. The Commanding Heights Strategy shapes China's response to the higher education trilemma of costs, access, and quality. Under this strategy, the unprecedented tertiary expansion has expanded college access, but at the expense of affordability, quality, and a large and increasing regional variation.