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Abstract Hepatocellular carcinoma (HCC) is a common malignant tumor with a complex immune evasion mechanism posing a challenge to treatment. The role of the S100A10 gene in various cancers has garnered significant attention. This study aims to elucidate the impact of S100A10 on CD8+ T cell exhaustion via the cPLA2 and 5-LOX axis, thereby elucidating its role in immune evasion in HCC. By analyzing the HCC-related data from the GEO and TCGA databases, we identified differentially expressed genes associated with lipid metabolism and developed a prognostic risk model. Subsequently, through RNA-seq and PPI analyses, we determined vital lipid metabolism genes and downstream factors S100A10, ACOT7, and SMS, which were significantly correlated with CD8+ T cell infiltration. Given the most significant expression differences, we selected S100A10 for further investigation. Both in vitro and in vivo experiments were conducted, including co-culture experiments of CD8+ T cells with MHCC97-L cells, Co-IP experiments, and validation in an HCC mouse model. S100A10 was significantly overexpressed in HCC tissues and potentially regulates CD8+ T cell exhaustion and lipid metabolism reprogramming through the cPLA2 and 5-LOX axis. Silencing S100A10 could inhibit CD8+ T cell exhaustion, further suppressing immune evasion in HCC. S100A10 may activate the cPLA2 and 5-LOX axis, initiating lipid metabolism reprogramming and upregulating LTB4 levels, thus promoting CD8+ T cell exhaustion in HCC tissues, facilitating immune evasion by HCC cells, ultimately impacting the growth and migration of HCC cells. This research highlights the critical role of S100A10 via the cPLA2 and 5-LOX axis in immune evasion in HCC, providing new theoretical foundations and potential targets for diagnosing and treating HCC.

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks.

Fresh puffer fish (Takifugu obscurus) are susceptible to microbial contamination and have a very short shelf-life of chilled storage. Hence, this study aimed to evaluate the effects of plasma-activated lactic acid (PALA) on microbiota composition and quality attributes of puffer fish fillets during chilled storage. The results showed that PALA treatment effectively reduced the growth of bacteria and attenuated changes in physicochemical indicators (total volatile basic nitrogen, pH value, K value, and biogenic amines) of puffer fish fillets. Additionally, insignificant changes were observed in lipid oxidation during the first 8 days (p > 0.05). Illumina-MiSeq high-throughput sequencing revealed that PALA effectively inhibited the growth of Pseudomonas in puffer fish fillets and maintained the diverse characteristics of the microbial community. In combination with sensory analysis, PALA extended the shelf life of puffer fish fillets for 4 days, suggesting that PALA could be considered a potential fish fillet preservation method.

A question-and-answer (Q&A) system for science and technology (S&T) policies and regulations plays a critical role in helping the public understand and apply these regulations. Large language models (LLM) can significantly enhance the accuracy and efficiency of such systems. However, current LLM-based S&T policy and regulation Q&A systems face several challenges: the lack of large-scale, high-quality datasets, insufficient methods for auto-matically constructing datasets with accurate policy and regulation knowledge integration, and issues with the professional accuracy and timeliness of the models’ knowledge updates. To address these challenges, this paper proposes a retrieval-augmented self-prompting method for constructing a high-quality, large-scale S&T policy and regulation Q&A dataset. Additionally, a Q&A system is developed, which combines an LLM optimized by low-rank adaptation (LoRA) techniques with an S&T policy and regulation knowledge base, and employs prompt learning techniques to guide the system in generating accurate answers. Experimental results demonstrate that the constructed Q&A dataset significantly improves the integration of policy and regulation knowledge compared with traditional methods. Furthermore, the proposed Q&A system outperforms general LLM-driven systems across various metrics, highlighting its enhanced performance in the domain of S&T policies and regulations.