Nanchang Institute of Technology

Epidermal growth factor receptor (EGFR) is overexpressed in most triple-negative breast cancer (TNBC) patients with poor prognosis; however, the therapeutic benefit of EGFR inhibitors (EGFRi) in breast cancer remains limited. In this study, we found poor response to EGFRi in TNBC was related to oxidative phosphorylation (OXPHOS) and breast cancer stem cells (BCSCs), and demonstrated that TDP43 (TAR DNA-binding protein 43) expression is positively correlated with non-response to EGFR tyrosine kinase inhibitors (EGFR-TKIs). TDP43 knockdown significantly enhances EGFR-TKI sensitivity and decreases EGFR-TKI resistance. Mechanistically, TDP43, a DNA/RNA-binding protein predominantly localized to the nucleus, translocates to mitochondria upon EGFR-TKI stimulation. The increased mitochondrial localization promotes OXPHOS, thereby enriching BCSCs and contributing to EGFR-TKI resistance. Inhibiting TDP43 expression or using our newly identified TDP43 inhibitor, atovaquone, suppresses OXPHOS and reduces EGFR-TKI resistance. Overall, our research identified TDP43 as a key regulator of EGFR-TKI sensitivity and resistance, and offers new therapeutic targets and promising application perspectives in TNBC.

Traditional distribution system state estimation methods struggle to achieve the effective coordination of the dynamic impact of the transmission system. Also, the data privacy breaches in power systems have become increasingly prominent. To address these challenges, this paper proposes a self-adaptive federated learning (SaFL)-based Dynamic-Static Integrated State Estimation (DSISE) method for the coordinated transmission-distribution (T&D) networks. A DSISE model is constructed where the Extended Kalman Filter (EKF)-based dynamic state estimation is for the transmission network, and the Weighted Least Square (WLS)-based static state estimation is for the multi-area three-phase unbalanced distribution network. Considering the privacy preservation of each area of the distribution network, a novel three-layer Federated Learning (FL) is proposed with an intermediate server layer to significantly reduce the communication cost, which readily emerges from the traditional two-layer FL. The proposed method is validated on the improved IEEE-39 and IEEE-123 T&D coordinated networks. Compared with existing FL, the proposed SaFL can improve the accuracy of voltage angle and magnitude state estimation by 10.73% and 13.49%, respectively.

With the development of big data technologies and the increasing adoption of blended learning in higher education, systematic and data-informed evaluation frameworks have become increasingly important. The rapid growth of business Japanese education in China further highlights the need for evaluation approaches that can account for both linguistic accuracy and humanistic values in professional communication. This study proposes a hybrid learning evaluation framework that integrates humanistic value considerations and develops a core linguistic analysis component based on an optimized Nivre dependency parsing algorithm. Rather than claiming a fully implemented evaluation system, the framework conceptualizes blended learning as the object of evaluation and aims to support teachers by providing structured insights into learners’ language use in business contexts. Drawing on learning theory, the study also discusses potential instructional implications for business Japanese learning from the perspectives of institutions, teachers, and students. Experimental results show that the optimized Nivre-based model achieves over 92% accuracy across multiple test sets in dependency analysis and performs effectively in detecting culturally relevant linguistic features such as honorific usage. It should be emphasized that this study primarily proposes a framework and validates the feasibility of its core computational module. The reported performance metrics reflect the module ‘s efficacy on specific linguistic analysis tasks, not an empirical evaluation of a complete pedagogical system or learning outcomes. The reported performance metrics reflect the technical capability of the core computational module and do not constitute empirical evidence of educational or instructional effectiveness.

The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.