Advancing Stepped-Waveform Radar Jamming Techniques for Robust False-Target Generation against LFM-CFAR Systems.

This study investigates the utilization of a stepped wave frequency modulation jamming technique in radar systems. The objective is to enhance the effectiveness and robustness of false target jamming in the presence of linear frequency modulation (LFM) radars employing constant false alarm rate (CFAR) detection. The proposed method combines stepped frequency modulation with full pulse delay/sum repeat jamming to enhance resilience against uncertainties in target parameters. Theoretical analysis and simulation experiments are conducted to establish relationships between key jammer parameters, such as frequency slope and power compensation, and performance metrics, like false target distribution and CFAR masking. The results demonstrate that the proposed technique effectively maintains a dense distribution of false targets surrounding the protected target, even in the presence of uncertainties in position and signal-to-noise ratio. In comparison to existing methods, the utilization of stepped-waveform modulation enables improved control over target distribution and CFAR masking. Adaptive power allocation compensates for parameter errors, thereby enhancing robustness. Simulation results reveal that the proposed approach significantly reduces the probability of detecting the true target by over 95% under uncertain conditions, while previous methods experienced degradation. The integration of stepped waveforms optimizes false target jamming, thereby advancing electronic warfare capabilities in countering advanced radar threats. This study establishes design principles for resilient jamming architectures and supports enhanced survivability against radars employing pulse compression and CFAR detection. Moreover, the concepts proposed in this study have the potential for extension to emerging radar waveforms.

Rutin ameliorates gout via reducing XOD activity, inhibiting ROS production and NLRP3 inflammasome activation in quail.

Gout is a metabolic disease affected by monosodium urate (MSU) deposition, which is directly related to hyperuricemia. Recent reports on the prevalence and incidence of gout have been widely circulated worldwide. Currently, the anti-gout drugs in clinical practice are mainly small-molecule synthetic drugs, and the effectiveness and safety are limited. Reducing uric acid and inhibiting inflammation are the focused areas of drug research and development on gout. Rutin, a natural flavonoid, has been reported to alleviate inflammation in various diseases. However, whether rutin exerts protective effects on gout remains to be elucidated. This study used quails without urate oxidase as experimental animals to induce endogenous gout models through a high purine diet. We confirmed that quail in the model group developed gout symptoms at 30 days of the experiment. And the targets of uric acid metabolism, oxidative stress level, and NLRP3 inflammasome were dysregulated in quails. Rutin treatment improves gout and reduces inflammatory expression in quail. We further confirmed that rutin treatment reduced XOD activity and uric acid levels in quail. And rutin inhibited ROS production, restored oxidative stress balance, inhibited NLRP3 inflammasome activation, and exerted anti-inflammatory effects. We extracted and identified the fibroblast-like synoviocytes (FLS) for the first time. The results showed that rutin could reduce ROS production and NLRP3 inflammasome activation of FLS after uric acid stimulation. In conclusion, our findings underscore that rutin may be a gout protective agent by reducing XOD activity, inhibiting ROS production and NLRP3 inflammasome activation. Meanwhile, this study also provides an available animal model for the research drugs of gout.

Overnutrition-induced gout: An immune response to NLRP3 inflammasome dysregulation by XOD activity increased in quail.

Background: Gout is a progressive metabolic disease closely related to hyperuricemia and urate deposition, with an increasing prevalence and incidence across the globe. Recent studies have shown that the pathological process of gout includes two stages: asymptomatic hyperuricemia and MSU crystal deposition. However, the immune response during the development of hyperuricemia to gouty arthritis is not fully elucidated. Methods: Thus, an overnutrition-induced whole-course gout model was established to clarify the immune response and pathological changes in the development from hyperuricemia to gouty arthritis. The quails without urate oxidase were used as experimental animals. And we confirmed that uric acid metabolic targets were changed when quails were in the asymptomatic hyperuricemia stage. Results: When the quail showed gout symptoms, the NLRP3 inflammasome was activated, and the expressions of IL-1ß, TNF-α, IL-6, IL-8, and IL-18 were significantly increased. The relationship between the uric acid metabolism target and the NLRP3 inflammasome may be the critical immune response between hyperuricemia and gouty arthritis. Our data showed that, in the process of gout disease, the expression of xanthine oxidase (XOD) has been increasing, which increases the level of uric acid, disrupts the balance of oxidative stress, generates a large amount of ROS, activates the NLRP3 inflammasome, and release IL-1ß. Treatment with the XOD inhibitor can reduce uric acid, restore the body's degree of peroxidative damage and antioxidant capacity, and inhibit NLRP3 inflammasome and IL-1ß. In vitro, we extracted and identified primary fibroblast-like synoviocytes (FLS) from quail for the first time. Stimulating FLS with uric acid also caused ROS release and NLRP3 inflammasome activation. However, treatment with an XOD inhibitor prevented all these responses in FLS. Conclusion: Our results indicate that the immune response between the uric acid metabolism target XOD and NLRP3 inflammasomes plays a crucial role in developing hyperuricemia to gouty arthritis, and inhibition of both XOD and NLRP3 inflammasomes may be an effective treatment for avoiding the development of asymptomatic hyperuricemia to MSU crystal deposition. Meanwhile, this study also provides an advantageous animal model for pathological mechanisms and research and development drugs for gout.

Smilax glabra Roxb.: A Review of Its Traditional Usages, Phytochemical Constituents, Pharmacological Properties, and Clinical Applications.

Smilax glabra Roxb. (SGB) is a medicinal plant widely distributed in 17 countries worldwide. It is the primary raw material of the world-famous and best-selling functional food and beneficial tea. SGB was first recorded in Ben Cao Jing Ji Zhu of the Southern and Northern Dynasties (420-589 AD) and was reported for nutritional and medicinal properties for thousands of years. This review searched PubMed, Web of Science, and other databases for relevant literature on SGB species until April 2022. It aims to provide more integrated thinking, detailed awareness, and better knowledge of SGB. More than 200 chemical components have been discovered, including flavonoids, phenolic, phenolic acids, stilbenes, organic acids, phenylpropanoids, and others. Previous studies have demonstrated that SGB and its active ingredients show a wide range of pharmacological effects, including anti-infective, anti-cancer, anti-inflammatory, antioxidant, cardiovascular protection, etc. However, many studies on the biological activity of this plant were mainly based on crude extracts and active ingredients, and there is a lack of clinical studies and toxicity studies to support the development of drug design, development, and therapy. In summary, this review will provide specific and valuable suggestions and guidelines for further research and application of this plant in the medicinal field.

PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images.

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images.

Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.