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Objectives: B7 homolog 3 protein (B7-H3, also known as CD276) is a member of the B7 family that has been found to be associated with the growth and progression of a variety of tumors, but no pan-cancer evaluations of CD276 have been performed so far. In this study, we aimed to perform a pan-cancer analysis of the oncogenic role of CD276 in human tumors; Methods: We used a series of databases to perform a pan-cancer analysis of CD276, including the expression level of CD276 in pan-cancer and its relationship to tumor progression, patient survival duration, the immune cell infiltration within the tumor, and the potential signaling pathways and molecular mechanisms associated with CD276; Results: We found that CD276 was a potential biomarker for the prognosis of most cancers. The high expression of CD276 was associated with tumor progression, leading to poor survival. Notably, the up-regulation of CD276 expression in tumors increased the tumor infiltration of cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs) and decreased the CD8+ T cells; Conclusions: Our study demonstrates that CD276 might promote tumor progression via the promotion of an immunosuppressive microenvironment. [ABSTRACT FROM AUTHOR]

Under hygrothermal environments, the structural stability and strength of all-fiber composite aircraft are significantly affected during long-term flight use. The wing skin, as a critical structural component, plays a vital role in bearing and transmitting aerodynamic loads. Therefore, it is crucial to investigate the structural compressive stability and strength of the wing skin throughout the aircraft's entire life cycle under these conditions. This study employs a real wing carbon fiber foam sandwich structure to investigate the compressive stability and strength of the wing skin structure of a new energy aircraft under actual flight conditions, specifically during the entire process of the room temperature dry state (RTD) and elevated temperature wet state (ETW). Initially, three-point bending tests were conducted on carbon fiber reinforced polymer (CFRP) laminates, foam cores, and CFRP reinforced foam sandwich structures. The CFRP laminates fully rebounded after bending damage in both the RTD and ETW environments. While CFRP reinforced foam sandwich structures also rebounded fully in the RTD environment, their rebound performance diminished in hygrothermal conditions due to the thermoplastic mobility of the foam cores, resulting in only weak rebound capabilities. In hygrothermal environments, the thermoplastic mobility of the foam core leads to diminished resilience after bending damage, resulting in only weak rebound capabilities. Subsequently, compressive instability tests were conducted on the wing skin foam sandwich structure. The results indicated that the basic test study effectively predicted the structural test outcomes. Structural components in the RTD environment exhibited overall flexural instability under compressive load, with damage morphology resembling a circular curve; the damaged specimens fully rebounded after unloading. Conversely, specimens in the ETW environment displayed localized instability, characterized by a wrinkled damage profile, resulting in only weak rebound capabilities after unloading. [ABSTRACT FROM AUTHOR]

Particle identification (PID) is one of the most fundamental tools in various physics research conducted in collider experiments. In recent years, machine learning methods have gradually become one of the mainstream methods in the PID field of high-energy physics experiments, often providing superior performance. The emergence of quantum machine learning may potential arm a powerful new toolbox for machine learning. In this work, targeting at the π±/K± discrimination problem at the STCF experiment, a convolutional neural network (CNN) in the endcap PID system is developed. By combining the hit position and arrival time of each Cherenkov photon at the sensors, a two-dimensional pixel map is constructed as the CNN input. The preliminary results show that the CNN model has a promising performance. In addition, based on the classical CNN, a quantum convolution neural network (QCNN) is developed as well, exploring possible quantum advantages provided by quantum machine learning methods.

In the environmental event of hazardous release into river, quick and accurate identification of the contamination source is important for emergence response. Generally, given a noisy and finite set of monitoring information, determining the source items (i.e. location, strength and release time) is an ill-posed inverse problem. In this study, a Markov chain Monte Carlo method combined with advection-dispersion equation (ADE) was proposed for the source identification of contamination event in river system based on a Bayesian probabilistic inferential framework. Case study with analytical solution for one-dimensional ADE showed that the proposed methodology was effective and the mean posterior errors for all source parameters were lower than 3%. Case simulation based on two-dimensional ADE with numerical solution obtained similar results and further demonstrated the utility of the proposed approach for source identification. We hope the study will provide a helpful guidance to develop approach for contamination event source identification to support environmental risk management of river system.