This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

Gliomas, the most prevalent and lethal form of brain cancer, are known to exhibit metabolic alterations that facilitate tumor growth, invasion, and resistance to therapies. Peroxisomes, essential organelles responsible for fatty acid oxidation and reactive oxygen species (ROS) homeostasis, rely on the receptor PEX5 for the import of metabolic enzymes into their matrix. However, the prognostic significance of PEX5 for glioma patients remains unclear. We developed a robust prognosis model based on PEX5-dependent signature. This signature not only serves as a robust prognosis model capable of accurately predicting outcomes for glioma patients but also effectively distinguishes several clinicopathological features, including the grade, isocitrate dehydrogenase (IDH) mutation, and 1p19q codeletion status. Furthermore, we developed a nomogram that integrates the prognostic model with other clinicopathological factors, demonstrating highly accurate performance in estimating patient survival. Patients classified into the high-risk group based on our prognostic model exhibit an immunosuppressive microenvironment. Finally, we validated that the peroxisomal localization of the signature genes depends on PEX5 and demonstrated that PEX5 is required for cell growth, migration and invasion of glioma cells. These findings identify the PEX5-dependent signature as a promising prognostic tool for gliomas.

The material undergoes high temperature and high strain rate deformation process during cutting process, may induce the dynamic recrystallization behavior and result the evolution of dynamic mechanical properties of the material to be machined. In this paper, the modified Johnson-Cook(J-C) model for Nickel based powder metallurgy superalloy considering dynamic recrystallization behavior in high strain rate and temperature is proposed. The dynamic mechanical properties of the material under different strain rates and temperature conditions are obtained by quasi-static compression test and split Hopkinson pressure bar (SHPB) test. The coefficients of the modified J-C model are obtained by linear regression method. The modified model is verified by comparison with experimental and model prediction results. The results show that the modified J-C model proposed in this paper can accurately describe the mechanical properties of nickel-based powder metallurgy superalloys at high temperature and high strain rate. Which provides help for studying the cutting mechanism and finite element simulation of nickel-based powder metallurgy superalloy.

Trophoblast cell-surface antigen 2 (Trop 2) is a transmembrane glycoprotein that is highly expressed in various cancer types with relatively low or no baseline expression in most of normal tissues. Its overexpression is associated with tumor growth and poor prognosis; Trop 2 is therefore, an ideal therapeutic target for epithelial cancers. Several Trop 2 targeted therapeutics have recently been developed for the treatment of cancers, such as anti-Trop 2 antibodies and antibody-drug conjugates (ADCs), as well as Trop 2-specific cell therapy. In particular, the safety and clinical benefit of Trop 2-based ADCs have been demonstrated in clinical trials across multiple tumor types, including those with limited treatment options, such as triple-negative breast cancer, platinum-resistant urothelial cancer, and heavily pretreated non-small cell lung cancer. In this review, we elaborate on recent advances in Trop 2 targeted modalities and provide an overview of novel insights for future developments in this field.

We analyzed the plant-litter-soil continuum to investigate the carbon and nitrogen distribution and ecological stoichiometry of an evergreen broad-leaved forest at Dagangshan Mountain, Jiangxi. The results showed that the average C and N contents and C:N ratios in the leaves and fine roots among 6 different tree species were 401.87g/kg, 21.41g/kg, 19.27 and 348.64g/kg, 15.73g/kg, 23.97, respectively; the average C and N contents and C:N ratios were 323.06 g/kg, 12.76 g/kg, 25.58 respectively in leaf litter, and 16.40 g/kg, 1.09 g/kg, 16.27 respectively for soil. In contrast with the C content, the total N content of the fine roots and litter had a high coefficient of variation and a high spatial heterogeneity. We ranked the six different representative tree species according to total C and N content in leaves and fine roots. The results for each species were generally consistent with each other, showing a positive correlation relationship between total C and N content in the leaves and roots. Among them, S. discolor (Champ. ex Benth.) Muell. plants displayed high carbon and nitrogen storage capacities, and on the other hand, C. fargesii Franch., C. myrsinifolia (Blume) Oersted, A. fortunei (Hemsl.) Makino, and V. fordii (Hemsl.) Airy Shaw showed a high nitrogen transfer rate. Total soil N and C decreased with depth. Soil organic carbon (SOC), soil resistant organic carbon (ROC), total N, alkali nitrogen, NH4+-N and NO3--N contents were all also negative correlated with soil depth, but the contents of the NH4+-N and NO3--N did not change significantly; The spatial distribution of soil NO3--N was significantly heterogeneous. At 0-10 cm soil depth, SOC was positively correlated with alkaline nitrogen, and at 10-20 cm soil depth, SOC was significantly positively correlated with total N. In general, when soil carbon was abundant, nitrogen supply capacity was also high.