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Background and aim: A surplus of glucocorticoids (GC) is a main cause of non-traumatic osteonecrosis of the femoral head (ONFH), and Jintiange (JTG), as one of the traditional Chinese medicines (TCM), also plays an instrumental role in the alleviation of bone loss simultaneously. Therefore, JTG was thought to be able to reverse GC-induced ONFH (GC-ONFH) to a certain extent. Experimental procedure: In vivo, the effect of JTG on trabeculae in the subchondral bone of the femoral head was investigated using micro-computed tomography (micro-CT), TdT-mediated dUTP nick end labeling (TUNEL) and histological staining; in vitro, proliferation, viability, apoptosis, and senescence of purified bone mesenchymal stem cells (BMSCs) were examined to demonstrate the direct impact of JTG on these cells. Meanwhile after using a series of interventions, the function of JTG on BMSC differentiation could be assessed by measuring of osteogenic and adipogenic markers at levels of protein and mRNA. Results: Our final results demonstrated that with the involvement of Wnt/β-catenin pathway, JTG was able to significantly promote osteogenesis, restrain adipogenesis, delay senescence in BMSCs, reduce osteoclast number, weaken apoptosis, and enhance proliferation of osteocytes, all of which could mitigate the progression of subchondral osteonecrosis. Conclusion: According to the results of experiments in vitro and vivo, JTG was deemed to relieve the early GC-ONFH using the prevention of destruction of subchondral bone, which was contributed to regulating the differentiation of BMSCs and the number of osteoclasts.

Abstract Pan-Immune-Inflammation Value (PIV) has recently received more attention as a novel indicator of inflammation. We aimed to evaluate the association between PIV and prognosis in septic patients. Data were extracted from the Medical Information Mart for Intensive Care IV database. The primary and secondary outcomes were 28-day and 90-day mortality. The association between PIV and outcomes was assessed by Kaplan–Meier curves, Cox regression analysis, restricted cubic spline curves and subgroup analysis. A total of 11,331 septic patients were included. Kaplan–Meier curves showed that septic patients with higher PIV had lower 28-day survival rate. In multivariable Cox regression analysis, log2-PIV was positively associated with the risk of 28-day mortality [HR (95% CI) 1.06 (1.03, 1.09), P

Abstract This paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model.

Key Clinical Message Leptospirosis is an important zoonosis worldwide. Due to nonspecific clinical manifestation and poor recognition in non‐epidemic area, there is often a delay in diagnosis and treatment. Early diagnosis from Metagenomic next‐generation sequencing test is crucial for timely intervention. Abstract We presented a case of a 19‐year‐old male patient who developed leptospirosis infection characterized by acalculous cholecystitis and septic shock after typhoon events. Metagenomic next‐generation sequencing (mNGS) helped to early diagnose leptospirosis infection. Finally, the patient achieved full recovery following the antibiotic treatment in addition to supportive care and was discharged.

The seven major river basins (the Yangtze River, the Yellow River, the Pearl River, the Songhua River, the Huai River, the Hai River and the Liao River) are the most important surface water resources in China, but there is a lack of quantitative analyses of water quality change trends, horizontal comparisons of governance effects, and systematic review of effective policies since the 21st century. Based on the water resources bulletin and environmental status bulletin issued by government departments, the changes in water quality, pollutant indicators and treatment effectiveness of seven major basins from 2001 to 2020 have been scientifically analyzed using mathematical and statistical methods. (1) Over the period 2001 to 2020, the overall water quality in the seven major river basins exhibited a gradual improvement. Different basins demonstrated varied growth values for Grade I-III water, reduction values for Grade IV-V, and inferior Grade V water. (2) Between 2001 and 2020, changes in sewage discharge volume and types led to adjustments in the main pollutant indicators of the seven basins. (3) The ranking of the pollution degree in the seven major basins exhibited dynamic changes but also remained relatively stable during specific periods or years. (4) Assessing the average annual growth rate of Grade I-III water and the average annual reduction rate of Grade IV-V and inferior Grade V water, the Huai River basin demonstrated the most outstanding governance effectiveness, while the Liao River basin, the Yellow River basin, and the Songhua River basin also achieved notable treatment results. (5) The improvement in water quality across the seven major river basins can be attributed to scientific planning, enhanced policies and regulations, surge in investment in water conservancy infrastructure, heightened environmental protection awareness, application of green production technology. To sum up, the research findings not only provide a scientific foundation for the governance and protection of the seven major basins but also offer a valuable reference for other developing countries to strike a balance between economic development and environmental protection.

Abstract For traction substation (TS), the harmonic current spectrum distribution of the left and right power supply sections has a large difference under different EMU operating conditions. This phenomenon will further affect the harmonic distribution of the entire traction power supply system and has the risk of harmonic distortion. At the same time, it may also cause interference to the communication line. In this paper, the transient direct current control technology is used to realize the converter of EMU, and then the TS side harmonic model under regenerative braking condition is realized. The harmonic problem, control process, and negative sequence problem caused by locomotive traction braking on the TS side under various working conditions are analyzed in detail. After connecting the C-type filter to the traction network side of the system, the THD (total harmonic distortion) is within the standard range, while the negative sequence problem of the system has worsened. Therefore, the parameters of the filter were optimized to improve both harmonics and negative sequence problems by combining the negative sequence characteristics under various operating conditions. The results show that the improved treatment can effectively control the harmonic problem.

Abstract Background Osteoarthritis is a degenerative joint disease. Cartilage degeneration is the earliest and most important pathological change in osteoarthritis, and persistent inflammation is one of the driving factors of cartilage degeneration. Cucurbitacin E, an isolated compound in the Cucurbitacin family, has been shown to have anti-inflammatory effects, but its role and mechanism in osteoarthritic chondrocytes are unclear. Methods For in vitro experiments, human chondrocytes were stimulated with IL-1β, and the expression of inflammatory genes was measured by Western blotting and qPCR. The expression of extracellular matrix proteins was evaluated by immunofluorescence staining, Western blotting and saffron staining. Differences in gene expression between cartilage from osteoarthritis patients and normal cartilage were analysed by bioinformatics methods, and the relationship between Cucurbitacin E and its target was analysed by a cellular thermal shift assay, molecular docking analysis and molecular dynamics simulation. For in vivo experiments, knee osteoarthritis was induced by DMM in C57BL/6 mouse knee joints, and the effect of Cucurbitacin E on knee joint degeneration was evaluated. Results The in vitro experiments confirmed that Cucurbitacin E effectively inhibited the production of the inflammatory cytokine interleukin-1β(IL-1β) and cyclooxygenase-2 (COX-2) by IL-1β-stimulated chondrocytes and alleviates extracellular matrix degradation. The in vivo experiments demonstrated that Cucurbitacin E had a protective effect on the knee cartilage of C57BL/6 mice with medial meniscal instability in the osteoarthritis model. Mechanistically, bioinformatic analysis of the GSE114007 and GSE117999 datasets showed that the PI3K/AKT pathway was highly activated in osteoarthritis. Immunohistochemical analysis of PI3K/Akt signalling pathway proteins in pathological slices of human cartilage showed that the level of p-PI3K in patients with osteoarthritis was higher than that in the normal group. PI3K/Akt were upregulated in IL-1β-stimulated chondrocytes, and Cucurbitacin E intervention reversed this phenomenon. The cellular thermal shift assay, molecular docking analysis and molecular dynamics experiment showed that Cucurbitacin E had a strong binding affinity for the inhibitory target PI3K. SC79 activated Akt phosphorylation and reversed the effect of Cucurbitacin E on IL-1β-induced chondrocyte degeneration, demonstrating that Cucurbitacin E inhibits IL-1β-induced chondrocyte inflammation and degeneration by inhibiting the PI3K/AKT pathway. Conclusion Cucurbitacin E inhibits the activation of the PI3K/AKT pathway, thereby alleviating the progression of OA. In summary, we believe that Cucurbitacin E is a potential drug for the treatment of OA.

Background: Oxidative stress is associated with frailty and adverse outcomes in chronic obstructive pulmonary disease (COPD). The oxidative balance score (OBS) assesses oxidative stress from diet and lifestyle, with higher OBS indicating more antioxidants than oxidants. A cross-sectional study was conducted to investigate the potential association between OBS and frailty in US adults with COPD. Methods: A total of 1201 COPD subjects from the National Health and Nutrition Examination Survey (NHANES 1999–2018) were assessed for frailty using the Frailty Index. OBS, consisting of 20 dietary and lifestyle factors, was the exposure variable. Weighted multiple logistic regression, subgroup analysis, and restricted cubic spline curves were used to assess the association between OBS and frailty. Results: Compared with the lowest OBS reference group (Q1), the adjusted odds ratios (ORs) for the highest quartile group (Q4) for OBS, dietary OBS, and lifestyle OBS were 0.41 (95% CI: 0.19–0.92), 0.37 (95% CI: 0.20–0.71), and 0.41 (95% CI: 0.24–0.71), respectively. All trend p-values were less than 0.05. Subgroup and RCS analyses revealed a negative linear association between OBS and frailty, with a significant reduction in frailty risk observed in women compared to men. Conclusions: OBS was negatively associated with frailty in COPD. The higher the OBS, the lower the risk of frailty, especially in women. Identifying at-risk populations with OBS and through antioxidant diet and lifestyle are potential ways to reduce the prevalence of frailty.

Abstract Background This study aims to explore the antibacterial activity of cethromycin against Staphylococcus aureus (S. aureus), and its relationship with multilocus sequence typing (MLST), erythromycin ribosomal methylase (erm) genes and macrolide-lincosamide-streptogramin B (MLSB) phenotypes of S. aureus. Results The minimum inhibitory concentrations (MICs) of cethromycin against 245 S. aureus clinical isolates ranged from 0.03125 to ≥ 8 mg/L, with the resistance of 38.8% in 121 methicillin-resistant S. aureus (MRSA). This study also found that cethromycin had strong antibacterial activity against S. aureus, with the MIC ≤ 0.5 mg/L in 55.4% of MRSA and 60.5% of methicillin-sensitive S. aureus (MSSA), respectively. The main MLSTs of 121 MRSA were ST239 and ST59, and the resistance of ST239 isolates to cethromycin was higher than that in ST59 isolates (P = 0.034). The top five MLSTs of 124 MSSA were ST7, ST59, ST398, ST88 and ST120, but there was no difference in the resistance of MSSA to cethromycin between these STs. The resistance of ermA isolates to cethromycin was higher than that of ermB or ermC isolates in MRSA (P = 0.016 and 0.041, respectively), but the resistance of ermB or ermC isolates to cethromycin was higher than that of ermA isolates in MSSA (P = 0.019 and 0.026, respectively). The resistance of constitutive MLSB (cMLSB) phenotype isolates to cethromycin was higher than that of inducible MLSB (iMLSB) phenotype isolates in MRSA (P

Abstract The human finger is the essential carrier of biometric features. The finger itself contains multi‐modal traits, including fingerprint and finger‐vein, which provides convenience and practicality for finger bi‐modal fusion recognition. The scale inconsistency and feature space mismatch of finger bi‐modal images are important reasons for the fusion effect. The feature extraction method based on graph structure can well solve the problem of feature space mismatch for the finger bi‐modalities, and the end‐to‐end fusion recognition can be realised based on graph convolutional neural networks (GCNs). However, this fusion recognition strategy based on GCNs still has two urgent problems: first, lack of stable and efficient graph fusion method; second, over‐smoothing problem of GCNs will lead to the degradation of recognition performance. A novel fusion method is proposed to integrate the graph features of fingerprint (FP) and finger‐vein (FV). Furthermore, we analyse the inner relationship between the information transmission process and the over‐smoothing problem in GCNs from an optimisation perspective, and point out that the differentiated information between neighbouring nodes decreases as the number of layers increases, which is the direct reason for the over‐smoothing problem. A modified deep graph convolution neural network is proposed, aiming to alleviate the over‐smoothing problem. The intuition is that the differentiated features of the nodes should be properly preserved to ensure the uniqueness of the nodes themselves. Thus, a constraint term to the objective function of the GCN is added to emphasise the differentiation features of the nodes themselves. The experimental results show that the proposed fusion method can achieve more satisfied performance in finger bi‐modal biometric recognition, and the proposed constrained GCN can well alleviate the problem of over‐smoothing.

Visual inspection of the appearance defects on industrial products has always been a research hotspot pursued by industry and academia. Due to the lack of samples in the industrial defect dataset and the serious class imbalance, deep learning technology cannot be directly applied to industrial defect visual inspection to meet the real application needs. Transfer learning is a good choice to deal with insufficient samples. However, cross-dataset bias is unavoidable during simple knowledge transfer. We noticed that the appearance defects of industrial products are similar, and most defects can be classified as stains or texture jumps, which provides a research basis for building a universal and adaptive industrial defect detection model. In this article, based on the idea of model-agnostic meta-learning (MAML), we propose an adaptive industrial defect detection model through learning from multiple known industrial defect datasets and then transfer it to the novel anomaly detection tasks. In addition, the Siamese network is used to extract differential features to minimize the influence of defect types on model generalization, and can also highlight defect features and improve model detection performance. At the same time, we add a coordinate attention mechanism to the model, which realizes the feature enhancement of the region of interest in terms of two coordinate dimensions. In the simulation experiments, we construct and publish a visual defect dataset of injection molded bottle cups, termed BC defects, which can complement existing industrial defect visual data benchmarks. Simulation results based on BC defects dataset and other public datasets have demonstrated the effectiveness of the proposed general visual detection model for industrial defects. The dataset and code are available at https://github.com/zhg-SZPT/MeDetection.

Long-landing Events (LLEs) can occur as a result of pilot’s improper operation, resulting in shorter available runways and higher operating costs. The LLE can be effectively pinpointed by analyzing data from the Quick Access Recorder (QAR), which records all of the pilot’s operations during takeoff and landing. Traditionally, domain experts inspect LLEs by manually setting thresholds on uni-dimensional data. However, they cannot detect effectively the defects caused by the pilot’s maneuvering technique because the potential mutual information between different features in the large amount of data is not considered. This paper proposes a data-driven LLE detection and causation analysis workflow, which can automatically mine and analyze the mutual information, to overcome the existing problems. Firstly, a dataset is established based on the extracted QAR data from 2002 flights, considering the landing phase of the aircraft. Subsequently, this paper proposes a Hybrid Feature Selection (HFS) method for selecting features that are highly correlated with LLEs in both supervised and unsupervised ways. A categorical Light Gradient Boosting Machine (LGBM) with a Bayesian optimization (LGBMBO) model is used to determine the performance improvement. Furthermore, the model is visualized to analyze the marginal effect of key parameters for the LLEs by using SHapley Additive exPlanations (SHAP). The experimental results demonstrate that our model reduces computational cost and achieves better performance. Additionally, this paper demonstrates that LLEs can be avoided during the landing phase by maintaining the appropriate descent speed, aircraft altitude, and descent angle.

A new methodology is proposed in this study for active steering of a tram with independently rotating wheels (IRWs). This methodology has been developed specifically to improve the tram’s steering capability and to reduce wheel-rail wear. The tram is represented by a complex dynamic model with a diverse range of operating conditions, and traditional active steering methods cannot ensure that the tram can remain well-controlled throughout the entire range of operating conditions. The linear parameter-varying (LPV)- H ∞ control method proposed in this paper uses variable parameter theory to represent the nonlinearity, the parameter uncertainty, and the external force changes in the IRW. The LPV- H ∞ steering controller can not only ensure the robustness of the uncertain system, but also can balance the complexity and the performance of the controller. This allows the tram to offer good steering performance under various running conditions. The simulation results presented in this research show that application of the proposed control method improves the running performance of the tram and reduces the wheel-rail wear significantly on both straight and curved tracks when compared with the passive case.

As the most abundant white blood cells in humans, neutrophils play a key role in acute and chronic inflammation, suggesting that these cells are a key component of targeted therapies for various inflammation-related diseases. Specific enzyme-responsive or specific ligand-modified polymer nanoparticles are beneficial for improving drug efficacy, reducing toxicity, and enhancing focal site retention. However, there remain significant challenges in biomedical applications of these synthetic polymer nanoparticles, mainly due to their rapid clearance by the reticuloendothelial system. In recent years, biomimetic drug delivery systems such as neutrophils acting directly as drug carriers or neutrophil-membrane-coated nanoparticles have received increasing attention due to the natural advantages of neutrophils. Thus, neutrophil-targeted, neutrophil-assisted, or neutrophil-coated nanoparticles exhibit a prolonged blood circulation time and improved accumulation at the site of inflammation. Despite recent advancements, further clinical research must be performed to evaluate neutrophil-based delivery systems for future biomedical application in the diagnosis and treatment of related inflammatory diseases. In this review, we have summarized new exciting developments and challenges in neutrophil-mediated drug delivery strategies for treating inflammation-related diseases.

Abstract Background Despite the dramatic advances in modern medicine, efficient therapeutic measures for renal fibrosis remain limited. Celastrol (CLT) is effective in treating renal fibrosis in rat models, while causing severe systemic toxicity. Thus, we designed a tubule-specific nanocage (K3-HBc NCs) that effectively deliver CLT to tubular epithelial cell in a virus-like manner. The targeting ligand (K3) to tubular epithelial cells was displayed on the surface of Hepatitis B core protein (HBc) NCs by genetic fusion to the major immunodominant loop region. Ultra-small CLT nanodots were subtly encapsulated into the cavity through electrostatic interaction with the disassembly and reassembly of K3-HBc NCs, to yield K3-HBc/CLT complex. The efficacy of K3-HBc/CLT NCs were demonstrated in Unilateral ureteral obstruction (UUO)-induced renal fibrosis. Results The self-assembled K3-HBc/CLT could specifically target tubular epithelial cells via affinity with K3 ligand binding to the megalin receptor, significantly attenuating renal fibrosis. Remarkably, K3-HBc/CLT NCs significantly increased therapeutic efficacy and reduced the systemic toxicity in comparison with free CLT in UUO-induced mouse renal fibrosis model. Importantly, analysis of RNA sequencing data suggested that the anti-fibrotic effect of K3-HBc/CLT could be attributed to suppression of premature senescence in tubular epithelial cells via p21Cip1 and p16Ink4a pathway. Conclusion The tubule-specific K3-HBc/CLT represented a promising option to realize precise treatment for renal fibrosis.

The burden charging operation in blast furnace is one of the most important operations during iron-making process. In this paper, we focus on the study of precise burden charging operation, which involves two aspects: How to obtain and form the optimal burden surface shape. For the first problem, we construct a mapping model between the burden surface characteristic parameters and the comprehensive operational performance indicators of the blast furnace, and transform the search of optimal burden surface shape into the target optimization problem. The second problem refers to establishing a suitable burden charging strategy based on the basic burden surface and the optimal burden surface. In our work, by adaptively adjusting the opening degree of the throttle valve, it is possible to control accurate burden volume during the rotation of the charging chute, which can make sure to spill the appropriate burden volume on the suitable charging units. In the simulation of experiments, we collected the real industrial data during iron-making process and demonstrated the efficiency of the proposed model.

Recognizing prohibited items in X-ray security checking images automatically based on Convolutional Neural Networks (CNNs) has attracted attention increasingly. However, there are no suitable X-ray security checking image databases to train a reliable CNN model. Therefore, we propose a data augmentation method for X-ray security checking images. First, a lot of new X-ray prohibited item images are generated using the improved Self-Attention Generative Adversarial Network (SAGAN). Next, a Cycle GAN based method is proposed to transform the item natural images into the X-ray images. It can enrich the diversity of the new images, including item shape and pose. Then, we combine the prohibited item images with background images to synthesize the new X-ray security checking images. Finally, two single shot multi-box detector (SSD) models are applied to verify whether the enlarged database has achieved data augmentation. Experimental results show that the performance of SSD model trained by the enlarged database is better than the SSD model trained by the original database. It implies that our method can achieve data augmentation for X-ray security checking images effectively.

Recognizing prohibited items automatically is of great significance for intelligent X-ray baggage security screening. Convolutional neural networks (CNNs), with the support of big training data, have been verified as the powerful models capable of reliably detecting the expected objects in images. Therefore, building a specific CNN model working reliably on prohibited item detection also requires large amounts of labeled item image data. Unfortunately, the current X-ray baggage image database is not big enough in count and diversity for CNN model training. In this paper, we propose a novel method for X-ray prohibited item data augmentation using generative adversarial networks (GANs). The prohibited items are first extracted from X-ray baggage images using a K-nearest neighbor matting scheme. Then, the poses of the obtained item images are estimated using a space rectangular coordinate system and categorized into four or eight classes for constructing a training database. For generating the realistic samples reliably, different GAN models are evaluated using Frechet Inception Distance scores, and some important tips of handling GAN training in X-ray prohibited item image generation are reported. Finally, to verify whether the generated images belong to its corresponding class or not, a cross-validation scheme based on a CNN model is implemented. The experimental results show that most of the generated images can be classified correctly by the CNN model. This implies that the generated prohibited item images can be used as the extended samples to augment an X-ray image database.

In terms of biometrics, a human finger itself is with trimodal traits including fingerprint, finger-vein, and finger-knuckle-print, which provides convenience and practicality for finger trimodal fusion recognition. The scale inconsistency of finger trimodal images is an important reason affecting effective fusion. It is therefore very important to developing a theory of giving a unified expression of finger trimodal features. In this paper, a graph-based feature extraction method for finger biometric images is proposed. The feature expression based on graph structure can well solve the problem of feature space mismatch for the finger three modalities. We provide two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion. The research results can not only promote the advancement of finger multimodal biometrics technology but also provide a scientific solution framework for other multimodal feature fusion problems. The experimental results show that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.

Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO.

Radiation-induced pulmonary fibrosis (RIPF) is a severe complication in patients treated with thoracic irradiation. Until now, there are no effective therapeutic drugs for RIPF. In the present study, we attempted to evaluate the effect of Magnesium isoglycyrrhizinate (MgIG) on RIPF, and to further explore the underlying mechanisms. We found that MgIG treatment markedly improved radiation-induced lung pathological changes, reduced collagen deposition, and decreased the transforming growth factor beta1 (TGF-β1) elevation induced by irradiation. In addition, MgIG treatment significantly relieved oxidative damage of pulmonary fibrosis in mice characterized by increased antioxidant factors expression and reduced oxidative factors expression. And, MgIG treatment also significantly reduced the production of intracellular reactive oxygen species (ROS) in vitro. Interestingly, administration of MgIG achieved lower expression levels of Nox4, and phosphorylation of p38MAPK and Akt in vivo and in vitro. Furthermore, treatment with MgIG notably reduced the expression levels of myofibroblast markers, Nox4, and phosphorylation of p38MAPK and Akt both in vivo and in vitro. More importantly, the inhibitory effects of MgIG on fibroblast differentiation were enhanced when the p38MAPK/Akt/Nox4 pathway was inhibited using their respective antagonists or Nox4 siRNA in vitro. Taken together, these findings suggested that MgIG could attenuate RIPF partly by inhibiting fibroblast differentiation, which was closely related to modulation of the p38MAPK/Akt/Nox4 pathway.

The addition of plasticizers makes plastics flammable, and thus, poses a potential risk to the environment. In previous researches, plasticizers with flame retardancy had been synthesized, but their eco-friendliness had not been tested or described. Thus, in this paper, eco-friendliness plasticizers with flame retardancy were designed based on phthalic acid esters (PAEs), which are known as common plasticizers and major plastic additives. For a comprehensive analysis, such as flammability, biotoxicity, and enrichment effects, 17 PAEs’ comprehensive evaluation values were calculated based on the ideal point method. Further, a multi-effect three-dimensional quantitative structure-activity relationship (3D-QSAR) model of PAEs’ flammability, biotoxicity and enrichment effects was constructed. Thus, 18 dimethyl phthalate (DMP) derivatives and 20 diallyl phthalate (DAP) derivatives were designed based on three-dimensional contour maps. Through evaluation of eco-friendliness and flammability, six eco-friendly PAE derivatives with flame retardancy were screened out. Based on contour maps analysis, it was confirmed that the introduction of large groups and hydrophobic groups was beneficial to the simultaneous improvement of PAEs’ comprehensive effects, and multiple effects. In addition, the group properties were correlated significantly with improved degrees of the comprehensive effects of corresponding PAE derivatives, confirming the feasibility of the comprehensive evaluation method and modified scheme.

Inflammation is critical to the pathogenesis of cardiovascular diseases (CVDs). We have uncovered intrauterine inflammation induced by lipopolysaccharide (LPS) increases CVDs in adult offspring rats. The present study aimed to explore the role of prenatal exposure to LPS on the lipid profiles in male offspring rats and to further assess their susceptibility to high fat diet (HFD). Maternal LPS (0.79 mg/kg) exposure produced a significant increase in serum and hepatic levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol, aspartate amino transferase as well as liver morphological abnormalities in 8-week-old offspring rats. Meanwhile, disturbed gene expressions involved in hepatic lipid metabolism and related signaling pathways were found, especially the up-regulated very-low density lipoprotein receptor (VLDLR) and down-regulated transmembrane 7 superfamily member 2 (TM7SF2). Following HFD treatment, however, the lipid profile shifts and liver dysfunction were exacerbated compared to the offsprings treated with prenatal LPS exposure alone. Compared with that in control offsprings, the hepatic mitochondria (Mt) in offspring rats solely treated with HFD exhibited remarkably higher ATP level, enforced Complex IV expression and a sharp reduction of its activity, whereas the offsprings from LPS-treated dams showed the loss of ATP content, diminished membrane potential, decline in protein expression and activity of mitochondrial respiratory complex IV, increased level of MtDNA deletion as well. Furthermore, treatment with HFD deteriorated these mitochondrial disorders in the prenatally LPS-exposed offspring rats. Taken together, maternal LPS exposure reinforces dyslipidemia in response to a HFD in adult offsprings, which should be associated with mitochondrial abnormalities and disturbed gene expressions of cholesterol metabolism.

Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency.

Passive source localization is a challenging task for one receiver, and the pressure sensor provides relatively simple information. An ocean-bottom seismometer (OBS) sensor placed on the seafloor surface can provide more information—not only pressure information, but also three-axis (x-, y-, and z-axis) velocity information at the seafloor interface. In this paper, an OBS sensor was used to estimate the position of the broadband sound source in a Pekeris shallow water waveguide with elastic bottom. As the dynamics that characterize ocean acoustic applications are inherently nonlinear, non-Gaussian, and non-stationary processes that quickly vary with space and time, sequential Bayesian filtering, such as particle filtering (PF), is able to adapt to these environmental changes. Simulation results show that the PF method with the vertical wave impedance (the ratio of the pressure and vertical particle velocity) in the frequency domain as a measurement vector is not affected by source depth and source spectrum information, making it more tolerant and more robust than that with pressure in positioning. Experimental data results verified the effectiveness of the PF method with the vertical wave impedance for the localization of the explosive source.

Background: Endothelial progenitor cells (EPCs) derived from the bone marrow (BM) play a key role in the homeostasis of vascular repair by enhanced reendothelialization. Panax notoginseng saponins (PNS), a highly valued traditional Chinese medicine, has been shown to reduce morbidity and mortality from coronary artery disease. The present research was designed to explore the contribution of progenitor cells to the progression of atherosclerotic plaques and the possible modulatory role of PNS in this process. Methods: PNS (60 or 120 mg/kg via intraperitoneal injection) was administered over 8 weeks in apolipoprotein E knockout mice on an atherogenic diet. The sizes and histochemical alteration of atherosclerotic lesions and numbers of EPCs in BM and peripheral blood were analyzed. The expression of chemokine stromal cell-derived factor 1α (SDF-1α) and its receptor, CXCR4, was monitored as well. Results: PNS significantly reduced the lesion area and intima-to-media ratio compared to vehicle treatment. PNS also augmented endothelialization and reduced the smooth muscle cell (SMCs) content of the lesions. The number of c-kit and sca-1 double-positive progenitor cells and flk-1 and sca-1 double-positive progenitor cells were significantly increased in the BM and the peripheral blood of the PNS-treated groups. PNS treatment increased the plasma levels of SDF-1α and SCF as well as the BM levels of matrix metalloproteinase-9 (MMP-9). Moreover, the mRNA levels of SDF-1α and protein levels of CXCR4 were both increased in the BM of mice treated with PNS, while SDF-1α expression decreased. Conclusion: PNS reduce the size of atherosclerotic plaques, and this effect appears to involve progenitor cell mobilization. SDF-1α-CXCR4 interactions and the possible modulatory role of PNS in this process may contribute to the increased progenitor cell mobilization.

BACKGROUND: Prenatal exposure to Lipopolysaccharide (LPS) produces hypertension in adult offspring rats. The present study was to explore the effects of prenatal inflammation on morphological and functional changes in the aorta from offspring rats and to further assess its susceptibility to cardiovascular diseases. METHODS AND RESULTS: Pregnant rats were treated intraperitoneally on gestation Days 8, 10 and 12 with saline, LPS (0.79 mg/kg), or pyrrolidine dithiocarbamate (PDTC, 100 mg/kg)+LPS, respectively. Aortic ring reactivity and histopathological alteration were analyzed in offspring at the age of 12 weeks. The detections of connexin (Cx) 37, Cx40, Cx43, and Cx45, including immunofluorescent patterns, protein levels and mRNA expression in the aorta, were performed as well. Furthermore, the expressions of Nuclear factor (NF)-κB (p65), IκBα, phospho-IκBα and IκBβ were determined. The results showed that prenatal LPS exposure leads to morphological abnormalities and impaired aortic reactivity in offspring. Prenatal LPS exposure also decreased the protein and mRNA expression of Cx37 in the aorta from offspring rats. NF-κB and phospho-IκBα levels were both increased, IκBα level, however, was decreased in the aorta of offspring from the maternal LPS exposure compared to the controls. Simultaneously, PDTC treatment markedly reversed the action of LPS. CONCLUSIONS: Decreased expression of Cx37 contributed to the aortic dysfunction of prenatal LPS exposure offspring, which should be associated with NF-κB activation.

X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people's lives and property. Due to the large number of parameters and high computational complexity, most current object detection models are challenging to deploy on portable mobile security inspection devices. Therefore, this paper proposes an improved YOLOv7-tiny model for application in prohibited item detection tasks. Firstly, the feature extraction backbone network is replaced with the lightweight GhostNet network to reduce computational complexity and improve detection speed. Secondly, the FPN in the Neck is replaced with BiFPN, further reducing computational complexity and memory access through skip connections. Finally, a lightweight CA attention mechanism is embedded between the Backbone and Neck layers, and the Focal-EIoU Loss function is employed to enhance the detection capability for small-sized items. Experimental results on the SIXray public dataset show a 14.8% reduction in model parameters, a 19.7% reduction in computational complexity, and a 15.9% reduction in volume after the improvements. The detection speed increases from 82.4 to 90.2, and the detection accuracy for prohibited items reaches 90.3%. The experimental results demonstrate that the improved model achieves overall lightweighting while maintaining a high detection rate and improving detection speed. [ABSTRACT FROM AUTHOR]