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C16H12F3NO, orthorhombic, P212121 (no. 19), a = 6.9928(6) Å, b = 8.9764(8) Å, c = 21.216(2) Å, V = 1331.7(2) Å3, Z = 2, Rgt(F) = 0.0583, wRref(F2) = 0.1552, T = 298 K.

In this paper, large eddy simulation (LES) was performed to investigate the shock wave/boundary layer interaction (SBLI) phenomenon in transonic compressor cascades with a chord Reynolds number of 2.12 × 106. A comprehensive analysis was conducted on both the SBLI structures inherent to the transonic compressor cascade and the coherent vortex structures within the boundary layer. The underlying mechanisms of the shock-induced boundary layer transition and the shock low-frequency unsteadiness in the transonic compressor cascade were elucidated through spectral and dynamic mode decomposition (DMD) analysis. The results revealed that boundary layer separation induced by the SBLI cannot reattach, leading to the formation of large-scale coherent vortex structures. Spectral analysis revealed that the shock-induced boundary layer transition in the transonic compressor cascade was dominated by inviscid Kelvin–Helmholtz (K–H) and secondary instability mechanisms, characterized by a dimensionless Strouhal number of 0.06. Additionally, pressure signals showed the variations in sub-frequency from the separated shear layer to the main flow. The oscillation amplitude of the shock foot was significantly greater than that of the shock main body, and the oscillation frequency of the shock foot was consistent with the sub-frequency. The oscillation frequency of the shock main body coincided with that of the compression ramp and flat plate configurations. Finally, DMD modal analysis indicated that high-frequency modes were correlated with turbulent fluctuations in the boundary layer, while medium- and low-frequency modes corresponded to shedding motion in the separated shear layer and low-frequency motion of the shock. This work promotes the understanding of the complex flow mechanisms of SBLI in the transonic compressor cascades. [ABSTRACT FROM AUTHOR]

Background Diabetic kidney disease (DKD) poses a significant challenge globally as a complication of diabetes. Hyaluronan (HA), a critical non-sulfated glycosaminoglycan in the extracellular matrix, plays a pivotal role in the progression of DKD. This study assesses the predictive significance of HA's corresponding receptor, RHAMM (receptor for HA-mediated motility), in DKD pathogenesis in type 2 diabetes (T2DM) patients. Methods Enzyme-linked immunosorbent assays were utilized to measure plasma and urine levels of HA, CD44 and RHAMM in 99 diabetic patients. Immunohistochemistry staining was employed to examine HA deposition, CD44 and RHAMM expressions from 18 biopsy-proven DKD patients. Spearman correlation analysis, linear regression and receiver operating characteristic (ROC) analysis were conducted to establish associations between plasma HA, CD44 and RHAMM levels, and clinical parameters in DKD patients with T2DM. Results Elevated plasma and urine HA, CD44 and RHAMM levels were notably observed in the severe renal dysfunction group. Plasma RHAMM exhibited positive correlations with HA (r = 0.616, P  < .001) and CD44 (r = 0.220, P  < .001), and a negative correlation with estimated glomerular filtration rate (eGFR) (r = –0.618, P  < .001). After adjusting for other potential predictors, plasma RHAMM emerged as an independent predictor of declining eGFR (β = –0.160, P  < .05). Increased HA, CD44 and RHAMM levels in kidney biopsies of DKD patients were closely associated with heightened kidney injury. The ROC curve analysis highlighted an area under the curve (AUC) of 0.876 for plasma RHAMM, indicating superior diagnostic efficacy compared to CD44 in predicting DKD pathogenesis. The combined AUC of 0.968 for plasma RHAMM, HA and CD44 also suggested even greater diagnostic potential for DKD pathogenesis. Conclusion These findings provide initial evidence that elevated RHAMM levels predict DKD pathogenesis in T2DM patients. The formation of a triple complex involving HA, CD44 and RHAMM on the cell surface shows promise as a targetable biomarker for early intervention to mitigate severe renal dysfunctions. [ABSTRACT FROM AUTHOR]

The effects of back pressure on the transonic cascade operating state are crucial and can determine the structure of internal shock waves and secondary flows. In this paper, numerical methods validated by experiments were employed to investigate the evolution mechanisms of the inlet flow field, shock structure, secondary flow structure, and cascade performance under different back pressures. Analysis revealed that transonic cascade exhibited unique incidence characteristics in the inlet flow field under both subsonic and supersonic regimes, although these two regimes involved different physical mechanisms. The results revealed that the operating state of the transonic compressor cascade under the unique incidence condition was influenced by the outlet back pressure, and there existed a critical static pressure ratio. The critical static pressure ratio shifted from 1.61 for two-dimensional flow to 1.37 for three-dimensional (3D) flow at M1 = 1.1, due to the corner separation and the characteristics of 3D shocks. The 3D shock structure exhibited a non-uniform distribution along the spanwise direction due to the influence of back pressure and the separated boundary layer. The vortex structures analysis revealed that the secondary flow structure on the sidewalls of the transonic compressor cascade was primarily dominated by corner vortices, whose formation mechanism was related to the interaction between the shock wave and the sidewall boundary layer. Additionally, this interaction also led to the formation of detached shock and lip shock vortex structures. Finally, loss analysis indicated that the wake region of the transonic cascade primarily includes six types of loss, and the total loss of the cascade decreased with the rise in back pressure. [ABSTRACT FROM AUTHOR]

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo- L 0 -norm fast iterative shrinkage algorithm network (pseudo- L 0 -norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo- L 0 -norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo- L 0 -norm regularization model. The network's parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo- L 0 -norm FISTA-net for AgileSAR imaging. [ABSTRACT FROM AUTHOR]

Background: Pyroptosis plays a pivotal role in the tumor immune microenvironment (TME) dynamics, particularly in non‐small cell lung cancer (NSCLC). The aim of our study was to explore its effects on tumor progression, TME patterns, and the efficacy of therapeutic interventions in NSCLC. Methods: Our investigation encompassed a thorough analysis of pyroptosis‐related genes (PRGs), integrating immunohistochemistry (IHC) data, TME characteristics, stemness indices, and anticancer drug sensitivities. We aimed to analyze mRNA expression profiles across various cancers, constructing benchmark datasets to assess the clinical significance of PRGs in NSCLC. This included evaluating their association with clinical responses and efficacy. Notably, both our and HPA IHC data demonstrated significantly elevated GSDMD‐N protein levels in lung squamous cell carcinoma (LUSC) tissues. Results: The expression of PRGs differed significantly between tumor and normal tissues across various cancers, as validated by IHC data, and was correlated with prognosis (p < 0.05). Moreover, our investigation revealed significant differences (p < 0.05) in the expression of the PRGs among distinct TME subtypes categorized as C1 (wound healing), C3 (inflammatory), C2 (IFN‐gamma dominant), C5 (immunological quiet), C4 (lymphocyte deficient), and C6 (TGF‐beta dominant). Additionally, our research on anticancer drug sensitivity uncovered compelling connections between specific anticancer medications and the expression of PRGs, including GSDMD, ELANE, IL18, and CHMP4A (p < 0.05). Conclusion: Our study provided valuable insights into the critical role of PRGs in TME modulation, tumor stemness, and anticancer drug sensitivity across diverse cancers. Our findings illuminate the intricate relationship between pyroptosis and the TME, offering new perspectives for enhancing NSCLC treatment and prognosis. [ABSTRACT FROM AUTHOR]