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Abstract CD44 plays a key role in the communication of CSCs with the microenvironment and the regulation of stem cell properties. UALCAN was used to analyze the expression of CD44 in bladder cancer (BLCA) and normal tissue. The UALCAN was utilized to analyze the prognostic value of CD44 in BLCA. The TIMER database was used to explore the relationship between CD44 and PD-L1; CD44 and tumor-infiltrating immune cells. The regulatory effect of CD44 on PD-L1 was verified by cell experiments in vitro. IHC confirmed the results of the bioinformatics analysis. GeneMania and Metascape were used to analyze protein–protein interaction (PPI) investigations and functional enrichment analysis. We found that BLCA patients with high CD44 expression had worse survival than those with low CD44 expression (P

In order to meet the rapid growth of railway transportation demand, the technology that can improve its capacity has been widely concerned. Virtual Coupling (VC) is a new technology to improve capacity by decreasing headways between successive trains. Its basic principle is to control the train formation operation in coordination with the goal of keeping the same speed under the support of data transmission technology such as train-to-train communication. Our paper first reviews the existing theoretical research on train formation operation control, which is divided into four categories: train following, feedback control, optimal control and computational intelligent method. Secondly, by reviewing the related projects in Europe and China, based on the scenario analysis method, five general scenarios and two emergency scenarios in the whole process of VC operation are sorted out. Then, an original, complete list of performance indicators (PIs) is proposed for evaluating the performance of VC in different scenarios. Additionally, the paper gives a brief insight into VC operation by providing an adequate context to understand the proposed PIs.

ObjectiveTo construct a reliable nomogram available online to predict the postoperative survival of patients with perihilar cholangiocarcinoma.MethodsData from 1808 patients diagnosed with perihilar cholangiocarcinoma between 2004 and 2015 were extracted from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database. They were randomly divided into training and validation sets. The nomogram was established by machine learning and Cox model. The discriminant ability and prediction accuracy of the nomogram were evaluated by concordance index (C-index), receiver operator characteristic (ROC) curve and calibration curve. Kaplan-Meier curves show the prognostic value of the associated risk factors and classification system.ResultsMachine learning and multivariate Cox risk regression model showed that sex, age, tumor differentiation, primary tumor stage(T), lymph node metastasis(N), TNM stage, surgery, radiation, chemotherapy, lymph node dissection were associated with the prognosis of perihilar cholangiocarcinoma patients relevant factors (P

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.

The arrival interval at high-speed railway stations is one of the key factors that restrict the improvement of the train following intervals. In the process of practical railway operation, sudden conflicts occur sometimes. Especially when the conflict arises at the station, because the home signal cannot be opened in time, the emergency may affect the adjustment of the train operation under the scheduled timetable, resulting in a longer train following interval or even delay. With the development of artificial intelligence and the deep integration of big data, the architecture of train operation control and dispatch integration is gradually improving from the theoretical point. Based on this and inspired by the Green Wave policy, we propose an integrated operation method that reduces the arrival interval by avoiding unnecessary stops in front of the home signal and increasing the running speed of trains through the throat area. It is a two-step optimization method combining both intelligent optimization and mathematical–theoretical analysis algorithms. In the first step, the recommended approaching speed and position are obtained by analytical calculation. In the second step, the speed profile from the current position to the position corresponding to the recommended approaching speed is optimized by intelligent optimization algorithms. Finally, the integrated method is verified through the analysis of two distinct case studies. The first case study utilizes data from the Beijing–Shanghai high-speed railway line, while the second one is based on the field test. The numerical result shows that the proposed method could save the entry running time effectively, compared with the normal strategy given by the train driver. The method can mitigate controllable conflict events occurring at the station and provides theoretical support for practical operation.

Abstract Background Colorectal cancer is one of the most common malignancy in the world. It has been reported that cancer stem cells (CSCs) serve as the primary drivers of tumorigenesis and tumor progression. There is an urgent need to explore novel molecules that regulate CSCs or their signatures. Increasing evidence has shown that miRNAs are involved in tumorigenesis and progression. Here, we aim to explore the regulatory effect and mechanism of miR-3065-3p on the stemness of colorectal cancer. Methods The expression of miR-3065-3p in colorectal cancer and the association of miR-3065-3p expression with prognosis of patients with colorectal cancer were analyzed using TCGA dataset or clinical cases. Gain or loss of function in different models, including colorectal cancer cell lines and orthotopic xenograft or liver metastatic mouse model, were used to investigate the effects of miR-3065-3p on colorectal cancer stemness and metastasis in vitro and in vivo. Cancer stemness was analyzed by detecting the ability of migration and invasion, NANOG, OCT4, and SOX2 expression, ALDH activity and sphere formation. In addition, the interaction of miR-3065-3p and cytokine receptor-like factor 1 (CRLF1) was analyzed theoretically and identified by the luciferase reporter assay. Moreover, the correlation between CRLF1 expression and miR-3065-3p was analyzed in colorectal cancer tissues. Finally, the effect of CRLF1 on the stemness and metastasis of colorectal cancer in vitro and in vivo was assessed. Results In this report, we found that miR-3065-3p was overexpressed in colorectal cancer and that its high expression was associated with poor prognosis of patients with colorectal cancer. miR-3065-3p promotes the stemness and metastasis of colorectal cancer. Furthermore, CRLF1 was the downstream target of miR-3065-3p and inhibited the stemness of colorectal cancer. In addition, CRLF1 expression was negatively correlated with miR-3065-3p in colorectal cancer tissues. And, CRLF1 mediated the effects of miR-3065-3p on promoting stemness of colorectal cancer cells. Conclusion Our data suggest that miR-3065-3p promoted the stemness and metastasis of colorectal cancer by targeting CRLF1. miR-3065-3p might serve as a promising prognostic marker as well as a therapeutic target for colorectal cancer.

Recombinant human peroxiredoxin-5 (hPRDX5), isolated from anti-cancer bioactive peptide (ACBPs), shows a homology of 89% with goat peroxiredoxin-5 (gPRDX5) and is reported to display anti-tumor activity in vivo. Herein, we explored the effect of hPRDX5 and the responsible mechanism in treating pancreatic cancer. Tumor-bearing mice were randomly divided into normal PBS group and treatment group (n=5; 10 mg/kg hPRDX5). Flow cytometry was employed to examine lymphocytes, myeloid-derived suppressor cell subsets, and the function proteins of natural killer (NK) cells in peripheral blood, spleen, and tumor tissues of mice. Western blot was used to measure the protein expressions of the key nodes in TLR4-MAPK-NF-κB signaling pathway. The rate of tumor suppression was 57.6% at a 10 mg/kg dose in orthotopic transplanted tumor mice. Moreover, the population of CD3+CD4+T cells, NK cells, and CD3+CD8+T cells was significantly increased in the tumor tissue of the hPRDX5 group, while the proportion of granulocytic-myeloid-derived suppressor cells decreased slightly. In addition, after treatment with hPRDX5, the percentage of NK cells in blood increased more than 4-fold. Our findings indicated that hPRDX5 effectively suppressed pancreatic cancer possibly via the TLR4-MAPK-NF-κB signaling cascade; hence hPRDX5 could be a prospective immunotherapy candidate for treating pancreatic cancer.

Tumor metastasis remains a major challenge for patients with breast cancer. Aberrant epigenetic factor lysine‐specific demethylase 6B (KDM6B) has been associated with tumor progression. Here, we show that KDM6B is significantly down‐regulated in human breast cancer tissues, and its low expression is associated with poor prognosis of patients with breast cancer. Furthermore, overexpression of KDM6B remarkably inhibited cell proliferation, invasion, migration and epithelial–mesenchymal transition markers of breast cancer cells in vitro and tumor growth and lung metastasis in vivo. Notably, the expression of KDM6B in breast cancer tissues was negatively correlated with that of β‐catenin, and overexpression of KDM6B decreased the expression of β‐catenin and its accumulation in the nucleus of breast cancer cells. Overall, our findings provide novel insights into suppression of metastasis of breast cancer cells by KDM6B via β‐catenin and suggest involvement of the KDM6B‐Wnt/β‐catenin axis in breast cancer progression.

APAP is one of the most commonly used antipyretic and pain medications, but excessive use can cause liver toxicity and damage. 3,4-dihydroxyphenylethyl alcohol glycoside (DAG) is a component isolated from Sargentodoxa cuneata known to have anti-apoptotic, anti-oxidation and anti-inflammatory effects. However, the effects of DAG on acute liver failure (ALF) are largely unknown. The purpose of this study is to study the protective effects and mechanism of DAG on APAP-induced ALF in mice. We established an ALF model in adult male pathogen-free C57BL/6 mice treated with APAP (300 mg/kg) by intraperitoneal injection and resolved by 24 h. Hematoxylin and eosin (HE) staining was used to evaluate the pathological changes in mouse liver tissue. The infiltration of neutrophils in liver tissue and reactive oxygen species (ROS) in AML12 cells were analyzed by flow cytometry. The levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutathione (GSH), malondialdehyde (MDA), catalase (CAT), and superoxide dismutase (SOD) were analyzed using relevant kits. Our results show that DAG reduced APAP-induced serum ALT and AST levels, histopathological changes, liver neutrophil infiltration and proinflammatory cytokines production, also attenuated the accumulation of MDA and the exhaustion of GSH, CAT and SOD. In vitro experiment indicated that DAG dose-dependently inhibited APAP-induced the levels of pro-inflammatory factors (IL-1β and IL18), and reactive oxygen species (ROS) and preventing GSH depletion in mouse AML12 hepatocytes. More interestingly, DAG inhibited the expression of ERK, HO-1, NLRP3, Caspase1 (p20) and Gasdermin-D and upregulated the expression of GPX4 in liver tissues and AML12hepatocytes. Therefore, our results indicate that DAG may act as a potential agent to treat ALF induced by APAP by inhibiting hepatocyte ferroptosis and pyroptosis.

The poor immunotherapy of pancreatic cancer is mainly due to its complex immunosuppressive microenvironment. The Mediterranean diet contributes to low cancer incidence. Hydroxytyrosol (HT) derived from olive oil has multiple health-promoting effects, but its therapeutic effect on pancreatic cancer remains controversial. Here, we evaluated the inhibitory effect of HT on mouse pancreatic cancer, and the effect of HT on the immune microenvironment. We found that HT can inhibit the proliferation of Panc 02 cells through signal transducer and activator of transcription (STAT) 3/Cyclin D1 signaling pathway. In the tumor-bearing mice treated with HT, the orthotopic pancreatic tumors were suppressed, accompanied by a decrease in the proportion of myeloid-derived suppressor cells (MDSCs) and an increase in the proportion of M1 macrophages. In addition, we found that HT inhibited the expression of immunosuppressive molecules in bone marrow (BM)-derived MDSCs, as well as down-regulated CCAAT/enhancer-binding protein beta (C/EBPβ) and phosphorylation of STAT3. Moreover, HT enhanced the anti-tumor effect of anti-CD47 antibody in vivo. HT combined with plumbagin (PLB) induced more Panc 02 cells death than HT or PLB alone. This combination therapy not only inhibited the accumulation of MDSCs, but also promoted the infiltration of CD4+ and CD8+ T cells in the tumors. In summary, HT is a potential immunomodulatory drug for the treatment of pancreatic cancer.

In this paper, we propose the motion recurring pattern analysis (MRPA) method for the lossless representation of a motion database at the segment level instead of the motion degree of freedom (DOF) level. First, we concatenate all the motions into a long sequence in the motion database, and we discover similar posture paths by building a matching trellis structure based on the randomized k-d tree. Second, horizontal segments of paths are suitably refined, based on a self-organizing map, to obtain the optimized segmentation for maximum compression gains. Third, by using the path as a connection agent, these segments are clustered into a forest of trees. With this forest structure, we obtain the prediction residuals (the differences between the nonroot branches and their parents), and the differences between neighboring residuals are encoded under floating-point compression. Relative to previous lossless compression methods, our approach can achieve a higher compression ratio with comparable decompression time costs.

As a new technology to improve transportation capacity by compressing train tracking interval, virtual coupling (VC) has attracted significant attention from both industry and academia. The traditional overspeed protection mechanism cannot guarantee the safe running for a train fleet. Therefore, in this article, we study two key problems of the overspeed protection mechanism: the limit speed calculation and the safe control for the protection of train in the formation when overspeed occurs. This article describes the calculation methods of limit speed difference (LSD) based on relative coordinates and a collision mitigation approach by minimizing the relative kinetic energy, respectively. Finally, the effectiveness of the proposed method is verified through numerical analysis. The proposed calculation method of limit speed can realize large headway at low speed and small headway at high speed, which can meet the time requirement of switch and route arrangement in station areas. The performance of the model predictive control (MPC) based approach is compared with other two control strategies, i.e. basic adaptive cruise control (ACC) and directly maximum braking control (DBC). The simulation results show that the MPC strategy has the best performance among these three strategies in reducing the total relative kinetic energy of virtually-coupled train formation, followed by the DBC control strategy, and the basic ACC control strategy needs to be improved.

In recent years, urban rail transit has been developing rapidly, which provides great convenience for passengers. Consequently more and more wireless hot spots are set up along the track. In our paper, the advantages of wireless positioning in urban rail transit are discussed firstly. Then a fingerprinting train positioning algorithm for metro based on deep learning is proposed. The model of fingerprinting positioning and the simulation environment are established to verify the effectiveness of the algorithm. Finally, the results satisfy the accuracy requirements for train positioning in the research on the energy-efficient operation of metro train and it can be used to provide position information for the energy-saving analysis.

Background/Aims: The bi-functional enzyme 6-phosphofructo-2-kinase/fructose-2, 6-biphosphatase-4 (PFKFB4) is highly expressed in many types of cancer and its requirement for tumor survival has been demonstrated in glioma, lung, and prostate cancers. However, whether PFKFB4 plays a role in the tumor metastasis remains uncertain. This study explores the role of PFKFB4 in tumor metastasis and its underlying mechanisms in breast cancer cells. Methods: The expression of PFKFB4 was first analyzed using the Cancer Genome Atlas (TCGA) dataset, and confirmed by immunohistochemical staining of tissue microarray and breast cancer tissues from patient samples. Gain- and loss-of- function approaches were used to investigate the effects of PFKFB4 on breast cancer cell migration in vitro. Orthotopic xenograft model and experimental metastasis model were used to assess the effects of PFKFB4 on breast cancer cell metastasis in vivo. ELISA and immunofluorescence staining were used to examine HA production. Quantitative RT-PCR and western blotting were used to explore the mRNA and protein levels of HAS2, respectively. Results: We found that PFKFB4 enhances the migration/invasiveness of breast cancer cells in vitro as well as in vivo. Notably, the effects of PFKFB4 on migration are mediated by induction of HAS2 expression and HA production. Moreover, PFKFB4-induced HAS2 up-regulation depends upon the activation of p38 signaling. Conclusion: PFKFB4 promotes the metastasis of breast cancer cells via induction of HAS2 expression and HA production in a p38-dependent manner. Therefore, the PFKFB4/p38/HAS2 signaling pathway may serve as a potential therapeutic target for metastatic breast cancer.

In this article, we consider the identification problem of a class of nonlinear multiple-input single-output output-error autoregressive systems. First, a recursive generalized least squares algorithm using the auxiliary model identification idea is developed. Then, using the filtering technique, the identification model is decomposed into a filtered sub-identification model and a noise sub-identification model. For solving the difficulties that the filter is unknown and the information vectors contain the unknown variables, the interactive estimation theory and the the idea of replacing the unknown variables with their corresponding estimates are employed: the recursive least squares method is again used for identifying the system and noise model parameters, and the parameter estimates of the noise model are used to construct the estimated filter. Finally, a nonlinear example is given to verify the effectiveness of the algorithms, and the simulation results show that the recursive least squares algorithm using the filtering technique can produce more accurate parameter estimates under larger noise variances.

Background: Ovarian cancer is a difficult and lethal illness that requires early detection and precise classification for effective therapy. Microarray technology has permitted the simultaneous assessment of hundreds of genes' expression levels, yielding important insights into the molecular pathways driving ovarian cancer. To reduce computational complexity and improve accuracy, choosing the most likely differential genes to explain the impacts of ovarian cancer is necessary. Medical datasets, including those related to ovarian cancer, are often limited in size due to privacy concerns, data collection challenges, and the rarity of certain conditions. Data augmentation allows researchers to expand the dataset, providing a larger and more diverse set of examples for model training. Recent advances in machine learning and bioinformatics have shown promise in improving ovarian cancer classification based on gene information. Methods: In this paper, we present an ensemble algorithm based on gene selection, data augmentation, and boosting approaches for ovarian cancer classification. In the proposed approach, the initial genetic data were first subjected to feature selection. Results: The target genes were screened and combined with data augmentation and ensemble boosting algorithms. From the results, the chosen ten genes could accurately classify ovarian cancer at 98.21%. Conclusions: We further show that the proposed algorithm based on clustering approaches is effective for real-world ovarian cancer data, with 100% accuracy and strong performance in distinguishing between distinct ovarian cancer subtypes. The proposed algorithm may help doctors identify ovarian cancer patients early and develop individualized treatment plans. [ABSTRACT FROM AUTHOR]