The influence of outer large-scale motions (LSMs) on near-wall structures in compressible turbulent channel flows is investigated. To separate the compressibility effects, velocity fluctuations are decomposed into solenoidal and dilatational components using the Helmholtz decomposition method. Solenoidal velocity fluctuations manifest as near-wall streaks and outer large-scale structures. The spanwise drifting of near-wall solenoidal streaks is found to be driven by the outer LSMs, while LSMs have a trivial influence on the spanwise density of solenoidal streaks, consistent with the outer LSM impacts found in incompressible flows (Zhou et al., J. Fluid Mech., vol. 940, 2022, p. A23). Dilatational motions are characterized by the near-wall small-scale travelling-wave packets and the large-scale parts in the outer region. The streamwise advection velocity of the near-wall structures remains at $16 \sim 18u_{\tau }$, hardly influenced by Mach numbers, Reynolds numbers and wall temperatures. The spanwise drifting of near-wall dilatational structures, quantified by the particle image velocimetry method, follows a mechanism distinct from solenoidal streaks. This drifting velocity is notably larger than those of the solenoidal streaks, and the influence of outer LSMs is not the primary trigger for this drifting.

The practical implementation of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained with the data of the restricted nonlinear (RNL) model is used to predict the normal velocity on a detection plane at $y^+=10$ in a turbulent channel flow, and the predicted velocity is used as wall blowing and suction for drag reduction. An active control test is carried out by using the well-trained CNN in direct numerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall shear stresses, respectively. Furthermore, we explore the online control of wall turbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its observation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchronously in a RNL flow field. The control strategy discovered by RL has similar drag reduction rates with those obtained previously by the established method. Also, the training cost decreases by over thirty times at $Re_{\tau }=950$ compared with the DNS-RL model. The present results provide a perspective that combining the RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical. Also, this approach can be easily extended to flows at higher Reynolds numbers.

This paper retrospectively analysed the prevalence of macrolide-resistant Mycoplasma pneumoniae (MRMP) in some parts of China. Between January 2013 and December 2019, we collected 4,145 respiratory samples, including pharyngeal swabs and alveolar lavage fluid. The highest PCR-positive rate of M. pneumoniae was 74.5% in Beijing, the highest resistance rate was 100% in Shanghai, and Gansu was the lowest with 20%. The highest PCR-positive rate of M. pneumoniae was 74.5% in 2013, and the highest MRMP was 97.4% in 2019; the PCR-positive rate of M. pneumoniae for adults in Beijing was 17.9% and the MRMP was 10.48%. Among the children diagnosed with community-acquired pneumonia (CAP), the PCR-positive and macrolide-resistant rates of M. pneumoniae were both higher in the severe ones. A2063G in domain V of 23S rRNA was the major macrolide-resistant mutation, accounting for more than 90%. The MIC values of all MRMP to erythromycin and azithromycin were ≥ 64 μg/ml, and the MICs of tetracycline and levofloxacin were ≤ 0.5 μg/ml and ≤ 1 μg/ml, respectively. The macrolide resistance varied in different regions and years. Among inpatients, the macrolide-resistant rate was higher in severe pneumonia. A2063G was the common mutation, and we found no resistance to tetracycline and levofloxacin.

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

The gas-particle flow with multiple dispersed solid phases is associated with a complicated multiphase flow dynamics. In this paper, a unified algorithm is proposed for the gas-particle multiphase flow. The gas-kinetic scheme (GKS) is used to simulate the gas phase and the multiscale unified gas-kinetic wave–particle (UGKWP) method is developed for the multiple dispersed solid particle phase. For each disperse solid particle phase, the decomposition of deterministic wave and statistic particle in UGKWP is based on the local cell's Knudsen number. The method for solid particle phase can become the Eulerian fluid approach at the small cell's Knudsen number and the Lagrangian particle approach at the large cell's Knudsen number. This becomes an optimized algorithm for simulating dispersed particle phases with a large variation of Knudsen numbers due to different physical properties of the individual particle phase, such as the particle diameter, material density, etc. The GKS-UGKWP method for gas-particle flow unifies the Eulerian–Eulerian and Eulerian–Lagrangian methods. The particle and wave decompositions for the solid particle phase and their coupled evolution in UGKWP come from the consideration to balance the physical accuracy and numerical efficiency. Two cases of a gas–solid fluidization system, i.e. one circulating fluidized bed and one turbulent fluidized bed, are simulated. The typical flow structures of the fluidized particles are captured, and the time-averaged variables of the flow field agree well with the experimental measurements. In addition, the shock particle–bed interaction is studied by the proposed method, which validates the algorithm for the polydisperse gas-particle system in the highly compressible case, where the dynamic evolution process of the particle cloud is investigated.

The Chief Officials’ Appearance System (COAS), introduced in 2015, requires government leaders to appear in court and explain their actions. Unlike other post-2014 legal reforms aimed at reducing political influence in administrative litigation, the COAS uniquely actively involves political officials. This approach is based on the belief that increased participation will help officials to gain a better understanding of public concerns and improve administrative litigation quality. However, few studies have examined the system's effectiveness, and existing research relies on anecdotal evidence with limited analysis. To address this gap, we conducted a systematic empirical inquiry using 1,551 administrative litigation cases filed in a Beijing local court and extensive field research in 12 other provinces. Contrary to official expectations, we found the system reproduced the administrative grievances it was tasked with resolving. Moreover, when chief officials appear in court, administrative litigation is characterized by a renewed triad of apathetic state agencies, increasingly agitated plaintiffs and strategically empowered courts.

The laboratory generation and diagnosis of uniform near-critical-density (NCD) plasmas play critical roles in various studies and applications, such as fusion science, high energy density physics, astrophysics as well as relativistic electron beam generation. Here we successfully generated the quasistatic NCD plasma sample by heating a low-density tri-cellulose acetate (TCA) foam with the high-power-laser-driven hohlraum radiation. The temperature of the hohlraum is determined to be 20 eV by analyzing the spectra obtained with the transmission grating spectrometer. The single-order diffraction grating was employed to eliminate the high-order disturbance. The temperature of the heated foam is determined to be T = 16.8 ± 1.1 eV by analyzing the high-resolution spectra obtained with a flat-field grating spectrometer. The electron density of the heated foam is about $N_e=4.0\pm 0.3\times {10}^{20}{\text{cm}}^{-3}$ under the reasonable assumption of constant mass density.

In preparation for an experiment with a laser-generated intense proton beam at the Laser Fusion Research Center at Mianyang to investigate the 11B(p,α)2α reaction, we performed a measurement at very low proton energy between 140 keV and 172 keV using the high-voltage platform at the Institute of Modern Physics, Lanzhou. The aim of the experiment was to test the ability to use CR-39 track detectors for cross-section measurements and to remeasure the cross-section of this reaction close to the first resonance using the thick target approach. We obtained the cross-section σ = 45.6 ± 12.5 mb near 156 keV. Our result confirms the feasibility of CR-39 type track detector for nuclear reaction measurement also in low-energy regions.

Objective. Postmenopausal women experiences osteoporotic structural damage and bone fragility resulting from reduced bone formation and increased bone resorption. Osteoporosis frequently affects the vertebral column and causes compression fractures. This study aims to characterize roles of miRNAs in osteoporosis and subsequent incidence risk of vertebral fractures for postmenopausal women. Methods. Differentially expressed miRNAs between osteoporotic patients with vertebral fractures and osteoporotic patients without fracture were identified. This retrospective study included 78 osteoporotic patients with vertebral fractures and 82 osteoporotic patients without vertebral fractures. The plasma levels of bone metabolic markers, 25-hydroxyvitamin D (25-(OH)VitD), propeptide of type I procollagen (PINP), and β-Carboxyl terminal peptide (β-CTx), were detected using the patented electro-chemiluminescence (ECLIA) method. The expression levels of miR-491-5p and miR-485-3p were determined by qRT-PCR. Pearson correlation analysis was carried out to assess the relationship between miR-491-5p, miR-485-3p, and bone metabolic markers. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the performance of miR-491-5p and miR-485-3p in diagnosing the occurrence of vertebral fractures in osteoporotic patients.Results: The plasma levels of PINP and β-CTx were elevated but the plasma level of 25-(OH)VitD was declined in osteoporotic patients with vertebral fractures when comparable to those without (< 0.05). The plasma expression levels of miR-491-5p and miR-485-3p were declined osteoporotic patients with vertebral fractures when comparable to those without (< 0.001). Pearson correlation analysis revealed that the relative expression level of miR-491-5p was negatively correlated with the level of 25-(OH)VitD (r = -0.518, < 0.001) but positively correlated with the levels of PINP (r = 0.547, < 0.001) and β-CTx (r = 0.380, < 0.001). We also observed a negative correlation between the relative expression level of miR-485-3p and 25-(OH)VitD (r = -0.388, < 0.001), a positive correlation between miR-485-3p and PINP (r = 0.422,< 0.001). ROC curves for prediction of vertebral fracture following osteoporosis in postmenopausal women by miR-491-5p expression yielded 0.866 AUC and by miR-485-3p expression produced 0.848 AUC. Conclusion. The data suggest that downregulated expressions of miR-491-5p and miR-485-3p may be involved in the occurrence of vertebral fractures in postmenopausal women with osteoporosis.

Polytypism is common in micas, and the frequency of polytype occurrence is believed to be related closely to the crystallization conditions and chemical compositions of the corresponding fluids and melts. Coexisting multiple standard and complex/disordered polytypes in igneous rocks generally reflect a complicated magma evolution history. The purpose of the current study was to clarify the origin of coexisting biotite polytypes and their growth mechanism. Micro-X-ray diffraction (μXRD) and transmission electron microscopy (TEM) were used to investigate Fe-rich biotite phenocrysts in rhyolite from the Long Valley Caldera, California, USA. The μXRD analyses characterized various polytypes, and TEM observations revealed that common polytypes (e.g. 1M, 2M1, and 3T) and rare polytypes (e.g. 4M2 and 4A8) coexist within biotite monocrystals. The two 4-layer polytypes of Fe-rich biotite, 4M2 and 4A8, were identified via selected-area electron diffraction (SAED) and high-resolution scanning transmission electron microscopy (HRSTEM) at the atomic resolution, with unique stacking sequences ([0222] for 4M2 and [002$\overline{2}$] for 4A8). Energy-dispersive X-ray spectroscopy (EDS) results showed differences in their chemical compositions, especially Fe and K. The 4A8 polytype is reported for the first time. The present study suggested that environmental changes, such as rapid cooling and inhomogeneous compositional distribution, led to chemical and structural oscillations and complex nucleation of the two 4-layer polytypes. Screw dislocations producing spiral growth enhance polytype stability and form ordered long-period/complex polytypes. These results are useful to understand the origin of long-period/complex polytypes and the intergrowths of diverse polytypes formed in non-equilibrium crystallization environments.

In order to minimize the initial energy storage of tens of MA-class Z-pinch accelerators, an intelligent optimization method was developed based on the transmission line code circuit model and PSOGSA algorithm. Using several input parameters, the four overall parameters of the Z-pinch accelerator could be fast determined, including the connection and parallel combination of LTD cavities, the outer radius of the stack-MITL system, and electrical length of monolithic radial transmission lines. The optimization method has been verified by comparing the results with the Z-300 and Z-800 conceptual designs. By means of this intelligent optimization, some factors that affect the initial energy storage on high-current Z-pinch accelerators have been investigated, such as the operating electrical fields, the diameter of the stack-MITL system, and the inner diameter of the LTD cavity. The suggestions for designing relatively low-cost, efficient LTD-based accelerators have been proposed.

Sepsis is a severe disease with high mortality, and liver injury is an independent risk factor for sepsis morbidity and mortality. We analyzed co-differentially expressed genes (co-DEGs) to explore potential biomarkers and therapeutic targets for sepsis-related liver injury. Three gene expression datasets (GSE60088, GSE23767, and GSE71530) were downloaded from the Gene Expression Omnibus (GEO). DEGs were screened between sepsis and control samples using GEO2R. The association of these DEGs with infection and liver disease was analyzed by using the CTD database. GO functional analysis, KEGG pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate the potential molecular mechanism of DEGs. DEGs of different tissues in GSE60088 were analyzed again to obtain specific markers of septic liver injury. Mouse model of sepsis was also established by cecal ligation and puncture (CLP), and the expression of specific markers in liver, lung, and kidney tissues was analyzed using Western blot. Here, we identified 21 DEGs in three datasets with 8 hub genes, all of which showed higher inference scores in liver diseases than bacterial infections. Among them, only TNFRSF1A had a liver-specific differential expression. TNFRSF1A was also confirmed to be specifically reduced in septic liver tissues in mice. Therefore, TNFRSF1A may serve as a potential biomarker for septic liver injury.

Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma.