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Gliomas are the most common malignant tumors of the brain. Immune checkpoints have been increasingly emphasized as targets for treating malignant tumors. B7‐H3 has been identified as an immune checkpoint that shows potential value for targeting therapies. We set out to characterize the expression pattern and biological function of B7‐H3 in brain gliomas using high‐throughput data obtained from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) projects. B7‐H3 was upregulated more in higher‐grade gliomas than that in lower‐grade gliomas in both CGGA and TCGA datasets. Isocitrate dehydrogenase (IDH) mutation seemed to exert significant influence on B7‐H3 expression in gliomas but led to quite different results between grade II gliomas and higher‐grade gliomas. In addition to IDH, methylation of B7‐H3 promoter and microRNA‐29 family also showed a potential regulatory effect on B7‐H3 expression. Gene ontology analysis revealed that B7‐H3 was associated with mitotic cell cycle, cell proliferation and immune response. Further investigation suggested that B7‐H3 was mostly involved in the Toll‐like receptor signaling pathway. Survival analysis indicated that B7‐H3 was an independent unfavorable prognosticator for glioma patients in both CGGA and TCGA datasets. B7‐H3 expression is regulated by multiple mechanisms and is potentially involved in the T‐cell receptor signaling pathway. Higher B7‐H3 expression indicates a worse prognosis for glioma patients, which warrants further research into the development of inhibitors for targeting this immune checkpoint, but we still need to be cautious about immune checkpoint inhibition for central nervous system tumors. B7‐H3 expression is regulated by multiple mechanisms and is mainly involved in the T‐cell receptor signaling pathway. Higher B7‐H3 expression indicates worse prognosis for glioma patients, which warrants further research into the development of inhibitors for targeting this immune checkpoint, but we still need to be cautious about immune checkpoint inhibition for central nervous system tumors.

Background Transmembrane 4 L six family member 1 (TM4SF1) is upregulated in several epithelial cancers and is closely associated with poor prognosis. However, the role of TM4SF1 and its potential mechanism in colorectal cancer (CRC) remain elusive. Methods We investigated the expression of TM4SF1 in the Oncomine, the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and confirmed the results by immunohistochemistry (IHC), qPCR and Western blotting (WB) of CRC tissues. The effect of TM4SF1 on the epithelial-to-mesenchymal transition (EMT) and cancer stemness of CRC cells was investigated by Transwell, wound healing and sphere formation assays. A series of in vitro and in vivo experiments were conducted to reveal the mechanisms by which TM4SF1 modulates EMT and cancer stemness in CRC. Results TM4SF1 expression was markedly higher in CRC tissues than in non-tumour tissues and was positively correlated with poor prognosis. Downregulation of TM4SF1 inhibited the migration, invasion and tumour sphere formation of SW480 and LoVo cells. Conversely, TM4SF1 overexpression significantly enhanced the migration, invasion and tumoursphere formation potential of CRC cells, Additionally, TM4SF1 silencing inhibited the EMT mediated by transforming growth factor-β1 (TGF-β1). Mechanistically, gene set enrichment analysis (GSEA) predicted that the Wnt signalling pathway was one of the most impaired pathways in TM4SF1-deficient CRC cells compared to controls. The results were further validated by WB, which revealed that TM4SF1 modulated SOX2 expression in a Wnt/β-catenin activation-dependent manner. Furthermore, we found that knockdown of TM4SF1 suppressed the expression of c-Myc, leading to decreased c-Myc binding to the SOX2 gene promoter. Finally, depletion of TM4SF1 inhibited metastasis and tumour growth in a xenograft mouse model. Conclusion Our study substantiates a novel mechanism by which TM4SF1 maintains cancer cell stemness and EMT via the Wnt/β-catenin/c-Myc/SOX2 axis during the recurrence and metastasis of CRC.

Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm. The proposed algorithm has been verified on 12 benchmark test functions and a multi-objective travelling salesman problem. The results demonstrate that the NSDP algorithm achieves better outcome in multiple performance metrics and higher solving efficiency, compared with non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization.

As the advanced functional materials, silver nanoparticles are potentially useful in various fields such as photoelectric, bio-sensing, catalysis, antibacterial and other fields, which are mainly based on their various properties. However, the properties of silver nanoparticles are usually determined by their size, shape, and surrounding medium, which can be modulated by various synthesis methods. In this review, the fabrication methods for synthesizing silver nanoparticles of different shapes and specific size are illustrated in detail. Besides, the corresponding properties and applications of silver nanoparticles are also discussed in this paper.

Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.

Contact and friction are closely related as friction cannot happen without contact. They are widely used in mechanical engineering, traffic, and other fields. The real contact surface is not completely smooth, but it is made up of a series of tiny contact asperities as viewed in the micro-scale. This is just the complexity of the contact and friction behaviors of rough surfaces: the overall mechanical behavior is the result of all asperities which are involved during the contact. Due to the multiplicity of surface topography, the complexity of contact scale, and the nonlinearity of the constitutive material, there are still many open topics in the research of contact and friction behavior of rough surfaces. Based on the perspective of the macroscopic and micro-nano scale contact mechanics, this review gives a brief overview of friction for the latest developments and points out the existing issues and opportunities for future studies.

The powertrain of a fuel cell vehicle typically consists of two energy sources: a proton electrolyte membrane fuel cell (PEMFC) stack and a battery package. In this paper, multi-dimensional dynamic programming (MDDP) is used to solve the energy management strategy (EMS) of fuel cell hybrid powertrain. This study built a fuel cell hybrid powertrain model, in which the battery model is built based on the Thevenin equivalent circuit. In order to improve the calculating efficiency and maintain the accuracy of the algorithm, the state variables in each stage are divided into primary and secondary. In the reverse solution process, the corresponding relationship between the multi state variables grid and the optimal cumulative function has been changed from three-dimensional to two-dimensional. The EMS based on MDDP is applied to component sizing of a commercial vehicle. Simulations were conducted using MATLAB under the C-WTVC working condition. By analyzing the fuel economy and system durability, the optimal component combination of comprehensive performance is obtained. Compared with the EMS based on dynamic programming (DP), the proposed method effectively improves the calculation accuracy: the hydrogen consumption can be reduced by 3.10%, and the durability of the fuel cell and battery can be improved by 1.08% and 0.13%, respectively.

Camera and LiDAR provide highly complementary information, and effective fusion of both modalities is desirable for 3D object detection. However, existing decision-level fusion methods mainly rely on the confidence of objects while neglecting the object uncertainty. To address this, we propose UDF-3D, an uncertainty-driven camera–LiDAR decision-level fusion method based on Dempster–Shafer evidence theory. First, object uncertainty is quantified by introducing the theory of subjective logic, where subjective opinions incorporate category belief masses and an uncertainty mass. Second, a cost matrix is designed for object matching, where each element is a weighted combination of geometric and semantic information from both sensors, and the weights are determined by the uncertainty parameters. Third, we construct a view-frustum constraint to re-evaluate unmatched objects, thereby reducing the false-negative rate. Finally, we design a novel evidence discounting factor within the Dempster–Shafer framework for matched objects, thereby mitigating cross-modal object conflicts during fusion and improving detection accuracy. Experiments on the KITTI dataset demonstrate that the proposed method outperforms existing decision-level fusion approaches, yielding improved detection accuracy.

Polymer semiconductors are promising candidates for wearable and skin-like X-ray detectors due to their scalable manufacturing, adjustable molecular structures and intrinsic flexibility. Herein, we fabricated an intrinsically stretchable n-type polymer semiconductor through spatial nanoconfinement effect for ultrasensitive X-ray detectors. The design of high-orientation nanofiber structures and dense interpenetrating polymer networks enhanced the electron-transporting efficiency and stability of the polymer semiconductors. The resultant polymer semiconductors exhibited an ultrahigh sensitivity of 1.52 × 10 4 μC Gy air −1 cm −2 , an ultralow detection limit of 37.7 nGy air s −1 (comparable to the record-low value of perovskite single crystals), and polymer film X-ray imaging was achieved at a low dose rate of 3.65 μGy air s −1 (about 1/12 dose rate of the commercial medical chest X-ray diagnosis). Meanwhile, the hybrid semiconductor films could sustain 100% biaxial stretching strain with minimal degeneracy in photoelectrical performances. These results provide insights into future high-performance, low-cost e-skin photoelectronic detectors and imaging. Wearable and skin-like X-ray detectors need intrinsic flexibility and high sensitivity. Here Guo et al. develop a stretchable n-type polymer semiconductor with aligned nanofiber networks which achieve photosensitivity of 1.52×10^4 μC Gyair^-1 cm^−2 and low detection limit of 37.7 nGyair s^-1.

Background Coronary heart disease (CHD) is a major complication of type 2 diabetes mellitus (T2DM), which causes an adverse prognosis. There is an urgent need to explore effective biomarkers to evaluate the patients’ adverse outcomes. Objective This study aimed to identify a novel indicator for screening T2DM and T2DM-CHD and predicting adverse prognosis. Materials and methods The study enrolled 52 healthy individuals, 85 T2DM patients, and 97 T2DM patients combined with CHD. Serum miR-199-3p levels in all study subjects were detected with PCR, and its diagnostic significance was evaluated by receiver operating curve (ROC) analysis. The involvement of miR-199-3p in disease development was assessed by the Chi-square test, and the logistic regression analysis was performed to estimate the risk factor for major adverse cardiovascular events (MACE) in T2DM-CHD patients. Results Significant downregulation of miR-199-3p was observed in the serum of both T2DM and T2DM-CHD patients, which discriminated patients from healthy individuals and distinguished T2DM and T2DM-CHD patients. Reduced serum miR-199-3p was associated with the increasing blood glucose, glycated hemoglobin (HbA1c), and homeostasis model assessment-insulin resistance index (HOMA-IR) of T2DM patients and the increasing triglycerides (TG), low-density lipoprotein (LDL), fibrinogen, and total cholesterol (TC) and decreasing high-density lipoprotein (HDL) of T2DM-CHD patients. miR-199-3p was also identified as a biomarker predicting the occurrence of MACE. Conclusion Downregulated miR-199-3p could screen the onset of T2DM and its complication with CHD. Reduced serum miR-199-3p was associated with the severe development of T2DM and T2DM-CHD and predicted the adverse outcomes of T2DM-CHD patients.