Search

Modelling train dynamics is essential for the platoon control of virtually coupled train sets (VCTS). Variable rail-wheel conditions and high data collection costs pose substantial challenges for both physics-based and data-driven models, especially when unit trains in the VCTS are heterogenous. This study introduces a train dynamics model that integrates parameter identification of basic resistance with physics-informed neural networks. The developed physics-informed neural network for train dynamics modelling (PINN-TDM) identifies key resistance parameters using the Davis formula. By combining the strengths of traditional modelling and data-driven approaches, the PINN-TDM provides accurate predictions of train dynamics, accommodating variations due to changes in rail-wheel conditions and heterogeneity among unit trains. Compared to conventional neural network models that lack physical guidance, the PINN-TDM demonstrates enhanced performance in data-limited conditions. Furthermore, this study incorporates the PINN-TDM into a model predictive control framework and derives the sufficient condition for heterogeneous string stability in the frequency domain using the Laplacian transformation. This enables the analysis of the impact of train dynamics heterogeneity on the stable region. Simulation experiments show that the integrated controller improves the precision of trajectory tracking and the stability of the VCTS under diverse rail-wheel conditions. [ABSTRACT FROM AUTHOR]

CD44 is a single-pass cell surface glycoprotein that is distinguished as the first molecule used to identify cancer stem cells in solid tumors based on its expression. In this regard, the CD44high cell population demonstrates not only the ability to regenerate a heterogeneous tumor, but also the ability to self-regenerate when transplanted into immune-deficient mice. However, the exact role of CD44 in cancer stem cells remains unclear in part because CD44 exists in various isoforms due to alternative splicing. Methods: Gain- and loss-of-function methods in different models were used to investigate the effects of CD44 on breast cancer stemness. Cancer stemness was analyzed by detecting SOX2, OCT4 and NANOG expression, ALDH activity, side population (SP) and sphere formation. Glucose consumption, lactate secretion and reactive oxygen species (ROS) levels were detected to assess glycolysis. Western blot, immunohistochemical staining, ELISA and TCGA dataset analysis were performed to determine the association of CD44ICD and PFKFB4 with clinical cases. A PFKFB4 inhibitor, 5MPN, was used in a xenograft model to inhibit breast cancer development. Results: In this report, we found that the shortest CD44 isoform (CD44s) inhibits breast cancer stemness, whereas the cleaved product of CD44 (CD44ICD) promotes breast cancer stemness. Furthermore, CD44ICD interacts with CREB and binds to the promoter region of PFKFB4, thereby regulating PFKFB4 transcription and expression. The resultant PFKFB4 expression facilitates the glycolysis pathway (vis-à-vis oxidative phosphorylation) and promotes stemness of breast cancer. In addition, we found that CD44ICD and PFKFB4 expressions are generally up-regulated in the tumor portion of breast cancer patient samples. Most importantly, we found that 5MPN (a selective inhibitor of PFKFB4) suppresses CD44ICD-induced tumor development. Conclusion: CD44ICD promotes breast cancer stemness via PFKFB4-mediated glycolysis, and therapies that target PFKFB4 (e.g., 5MPN therapy) may lead to improved outcomes for cancer patients.

JMJD3 (Jumonji domain containing-3), a histone H3 Lys27 (H3K27) demethylase, has been reported to be involved in the antigen-driven differentiation of germinal center B-cells. However, insight into the mechanism of JMJD3 in DLBCL (Diffuse large B-cell lymphoma) progression remains poorly understood. In this study, we investigated the subtype-specific JMJD3-dependent survival effects in DLBCL. Our data showed that in the ABC subtype, silencing-down of JMJD3 inhibited interferon regulatory factor 4 (IRF4) expression in a demethylase activity-dependent fashion. IRF4 reciprocally stimulated expression of JMJD3, forming a positive feedback loop that promoted survival in these cells. Accordingly, IRF4 expression was sufficient to rescue the pro-apoptotic effect of JMJD3 suppression in the ABC, but not in the GCB subtype. In contrast, ectopic overexpression of BCL-2 completely offset JMJD3-mediated survival in the GCB DLBCL cells. In vivo, treatment with siRNA to JMJD3 reduced tumor volume concordant with increased apoptosis in either subtype. This suggests it is a common target, though the distinctive signaling axes regulating DCBCL survival offer different strategic options for treating DLBCL subtypes.

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations. [ABSTRACT FROM AUTHOR]

Virtual coupling (VC) brings unprecedented opportunities for the train operation system by controlling multiple trains as a virtually coupled train set (VCTS) via train automation and communication. To deal with communication delays and small disturbances in a VCTS, this paper developed a tube‐based control approach for the VCTS, focusing on optimizing the control performances and meanwhile guaranteeing the individual and string stability. Specifically, a tube model predictive control (MPC) framework is constructed to handle safety constraints and regulate bounded small disturbances. Then, the individual stability and string stability are ensured by designing the constraint sets for inputs and proper coefficient tuning within the stable region. Finally, simulation‐based experiments verify that the proposed approach shows better robustness and higher efficiency, which can regulate train states within a tube, and the VCTS can be asymptotically stable and string stable facing disturbances and delays. [ABSTRACT FROM AUTHOR]

Whether proline deficiency is a metabolic vulnerability in colorectal tumors is unknown. The aim of this study was to investigate the effects of proline metabolism‐related genes and exogenous proline on the progression of colorectal cancer (CRC). We aimed to further clarify the role of pyrroline‐5‐carboxylate reductase (PYCR) 2, a key enzyme of proline synthesis, in the regulation of colorectal intraperitoneal metastatic tumors. This study was carried out based on The Cancer Genome Atlas (TCGA) data, database analysis, single‐cell functional analysis, tissue microarray, cell experiments, and animal models. We found that, PYCR2 mRNA and protein levels were upregulated in CRC. The mRNA level of PYCR2 was closely related to the prognosis and tumor metastasis of CRC patients. The upregulated PYCR2 expression was at least partly due to low promoter methylation levels. The nomogram constructed based on PYCR2 expression and clinical characteristics of CRC showed good accuracy in predicting lymph node metastasis. Pycr2 knockdown inhibited epithelial–mesenchymal transition (EMT) of mouse CRC cells. Proline supplementation did not rescue the inhibition of mouse CRC cell proliferation and migration by Pycr2 knockdown. Proline supplementation also did not rescue the suppression of subcutaneous tumors and intraperitoneal metastatic tumors in mice by Pycr2 knockdown. PYCR2 co‐expressed genes in TCGA‐CRC were enriched in epigenetic modification‐related biological processes and molecular functions. Four small molecules with the lowest binding energy to the PYCR2 protein were identified. Collectively, Pycr2 knockdown inhibited mouse CRC progression in a proline‐independent approach. PYCR2 may be a promising tumor metastasis predictor and therapeutic target in CRC. [ABSTRACT FROM AUTHOR]