Search Results (922)

Intravascular aspiration thrombectomy catheters are widely used to treat stroke, pulmonary embolism, and deep venous thrombosis. However, their performance is frequently compromised by clot material becoming lodged within the catheter tip. To address this, we develop a novel ultrasound-enhanced aspiration catheter approach that generates cavitation within the tip to mechanically degrade clots, with a view to facilitate extraction. The design employs hollow cylindrical transducers that produce inwardly propagating cylindrical waves to generate sufficiently high pressures to perform histotripsy. This study investigates the feasibility of self-sensing cavitation detection by analyzing voltage signals across the transducer during treatment. Experiments were conducted for two transmit pulse lengths at varying driving voltages with water or clot in the lumen. Cavitation clouds within the lumen were assessed using 40 MHz ultrasound imaging. Changes in the signal envelope during the pulse body and ringdown phases occurred above the cavitation threshold, the latter being associated with more rapid wave damping in the presence of bubble clouds within the lumen. In the frequency domain, voltage-dependent cavitation signals—subharmonics, ultra-harmonics, and broadband—emerged alongside transmit pulses. This work demonstrates a highly sensitive, sensor-free method for detecting cavitation within the lumen, enabling feedback control to further improve histotripsy-assisted aspiration. Full article

Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is proposed. Firstly, the bionic retina dual-channel module is embedded in the encoder to simulate the separation and processing mechanism of light and dark signals by biological vision systems and enhance the feature extraction ability of fuzzy target contours and translucent tissues. Secondly, the dynamic prompt module is introduced, and the response of key features is enhanced by inputting adaptive prompt templates to suppress the noise interference of water bodies. Finally, the edge prior guidance mechanism is integrated into the decoder, and low-contrast boundary features are dynamically enhanced by conditional normalization. The experimental results show that RetinalCoNet is superior to other mainstream segmentation models in the key indicators of mDice, reaching 82.3%, and mIou, reaching 89.2%, and it is outstanding in boundary segmentation in many different scenes. This study achieves accurate fish segmentation in complex underwater environments and contributes to underwater ecological monitoring. Full article

A comprehensive overview is provided on factors and processes influencing the final quality of a cup of coffee, with an emphasis on the brewing method’s central role. Coffee quality assessment, both at the bean and cup level, combines objective parameters (color, moisture, bean defects, density) with a notable degree of subjectivity, as consumer sensory perception is ultimately decisive. The brewing technique is described as a critical determinant of the final chemical, physical, and sensory attributes. Key parameters such as aroma profile, pH, titratable acidity, total and filtered solids, lipid and fatty acid content, viscosity, foam (crema), and colorimetric indices are detailed as essential metrics in coffee quality evaluation. Roasting creates most of coffee’s key aroma compounds. The brewing method further shapes the extraction of both volatile and other bioactive compounds like caffeine, chlorogenic acids, and lipids. Brewing methods significantly affect acidity, “body,” and crema stability, while water quality, temperature, and pressure are shown to impact extraction results and sensory properties. Attention is paid to how methods such as Espresso, filter, French press, and cold brew yield distinct physicochemical and sensory profiles in the cup. Overall, the review highlights the multifaceted nature of coffee cup quality and the interplay between raw material, processing, and preparation, ultimately shaping the coffee sensory experience and market value. Full article

The seed residue of Perilla frutescens possesses diverse biological properties and is rich in bioactive phytochemicals, including luteolin, rosmarinic acid, and apigenin. The aim of this study was to investigate the anti-diabetic effects of perilla seed residue crude extract (PCE) and its impact on the composition of the gut microbiome in rats with diabetes induced by a high-fat diet (HFD) and streptozotocin (STZ). Forty male Wistar rats were fed on an HFD for six weeks before receiving an injection of STZ injection to induce diabetes. These rats were then treated for four weeks with metformin (100 mg/kg bw) or PCE (100 and 1000 mg/kg bw) alongside a control group maintained on a normal diet. The results showed that PCE treatment improved metabolic parameters in diabetic rats, as evidenced by reduced water and food intake, increased body weight gain, lower blood glucose levels, and enhanced insulin secretion effects, especially at the 100 mg/kg bw dosage. PCE also promoted the regeneration of pancreatic β-cells and improved utilization of glucose. PCE also suppressed inflammation and oxidative stress, enhanced antioxidant capacity, and reduced circulating triglyceride levels. Most notably, PCE administration increased gut microbial diversity and shifted the microbiome closer to that of healthy controls, demonstrating its prebiotic effect. It promoted the abundance of beneficial bacteria that are linked to improved glucose metabolism and reduced inflammation—specifically, Bacteroides fragilis, Lactobacillus, Clostridium, and Akkermansia. Harmful bacteria associated with inflammation and poor glycemic control were reduced. Collectively, these results suggest that PCE not only helps restore a balanced gut microbiome but also offers metabolic benefits that could improve diabetic outcomes. These findings position PCE as a promising supplement for the management of diabetes and encourage further exploration of the mechanisms associated with its actions. Full article

Background and Clinical Significance: Non-palpable migrated contraceptive implants pose significant challenges for removal and are associated with neurovascular complications. Traditional open surgery near nerves is associated with postoperative morbidity. Migrated or deeply embedded implants near critical structures can result in severe complications, such as neuropathy, and their removal typically requires open surgical intervention. Case Presentation: We report a novel, minimally invasive, ultrasound (US)-guided technique for removing a migrated etonogestrel Implanon^® implant that caused ulnar neuropathy. A 38-year-old woman presented with severe neuropathic pain and paresthesia (NPRS 10/10; QuickDASH 55) along her left ulnar nerve following multiple failed removal attempts that induced deep migration. US confirmed the proximity of the implant to the ulnar nerve. Initial US-guided removal exacerbated her symptoms. Hydrodissection (HD) with 50 mL of 5% dextrose in water (D5W) without local anesthetic (LA) was performed to reduce inflammation and achieve separation. The implant migrated proximally during extraction. An additional HD with 50 mL of D5W without LA distally repositioned the implant. Percutaneous stabilization using a 25-gauge needle enabled secure removal. The intact 4 cm implant was extracted under real-time US guidance without open surgery. The patient experienced immediate symptom relief (NPRS 2/10; QuickDASH 4.5 at one month) and full resolution (NPRS 0/10; QuickDASH 0) with no motor deficits at one year. Conclusions: This case represents the first documented percutaneous removal of a nerve-adherent implant using combined US-guided D5W HD and needle stabilization, marking a paradigm shift in the management of such cases. This approach confirms the safety of US-guided foreign body removal using HD for nerve-adjacent implants and demonstrates the efficacy of combining D5W HD with needle stabilization. Surgical morbidity was avoided, while excellent long-term outcomes were achieved. Full article

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring. Full article

Intensified by climate change and anthropogenic activities, flood disasters necessitate rapid and accurate mapping for effective disaster management. This study develops an integrated framework leveraging synthetic aperture radar (SAR) and cloud computing to enhance flood monitoring, with a focus on a 2024 extreme rainfall event in Liaoning Province, China. Utilizing the Google Earth Engine (GEE) platform, we combine three complementary techniques: (1) Otsu automatic thresholding, for efficient extraction of surface water extent from Sentinel-1 GRD time series (154 scenes, January–October 2024), achieving processing times under 2 min with >85% open-water accuracy; (2) random forest (RF) classification, integrating multi-source features (SAR backscatter, terrain parameters from 30 m SRTM DEM, NDVI phenology) to distinguish permanent water bodies, flooded farmland, and urban areas, attaining an overall accuracy of 92.7%; and (3) Fuzzy C-Means (FCM) clustering, incorporating backscatter ratio and topographic constraints to resolve transitional “mixed-pixel” ambiguities in flood boundaries. The RF-FCM synergy effectively mapped submerged agricultural land and urban spill zones, while the Otsu-derived flood frequency highlighted high-risk corridors (recurrence > 10%) along the riverine zones and reservoir. This multi-algorithm approach provides a scalable, high-resolution (10 m) solution for near-real-time flood assessment, supporting emergency response and sustainable water resource management in affected basins. Full article

Single-factor and orthogonal experiments were conducted to investigate the biological characteristics of Dichomitus squalens strains isolated from wild fruiting bodies collected in Tekes County, Xinjiang Uygur Autonomous Region. Building upon the optimal mycelial culture conditions identified, domestication cultivation studies were performed, including experiments to induce fruiting body formation. Liquid strains were inoculated into substrates to monitor developmental stages from primordia formation to mature fruiting bodies, with macroscopic characteristics recorded throughout the cultivation process. Crude polysaccharides were extracted from the cultivated fruiting bodies using the water extraction and ethanol precipitation method. The scavenging rates of these polysaccharides against hydroxyl radicals (OH⁻) and superoxide anion radicals (O₂⁻) were measured to evaluate their in vitro antioxidant activity. Results demonstrated that the optimal growth conditions for D. squalens were as follows: sucrose as the preferred carbon source, yeast extract powder as the optimal nitrogen source, a pH of 5.0, and a temperature of 30 °C. Among these factors, pH exerted the most significant influence on the mycelial growth rate, followed by nitrogen source, carbon source, and temperature. Mature fruiting bodies developed approximately 57 days after inoculation with liquid strains. The crude polysaccharide extraction yield from the cultivated fruiting bodies reached 7.07%, with a total polysaccharide content of 24.69% in the extract. The crude polysaccharides exhibited potent radical scavenging activity: at a concentration of 5.0 mg/mL, the hydroxyl radical scavenging rate was 56.74%, while the superoxide anion radical scavenging rate reached 78.3%. These findings indicate that D. squalens possesses significant antioxidant potential. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Agricultural workers in the United States (U.S.) are exposed to occupational heat stress, increasing their risk of adverse kidney outcomes. The aim of this integrative review was to explore the relationship between occupational heat stress and kidney health among U.S. agricultural workers. PubMed, EMBASE, Scopus, and Google Scholar were searched for original research articles on this relationship among U.S. agricultural workers. Studies were screened and reviewed by two independent reviewers in three phases: title and abstract screening, full text screening, and data extraction. The search yielded 278 articles; 14 were included in the final analysis. Heat stress was commonly measured using core body temperature changes, heat index, and wet-bulb globe temperature. Acute kidney injury (AKI) incidence following a single work shift was up to 43%. Occupational heat stress and piece-rate compensation increased the odds for developing AKI. The use of cooling bandanas and water mixed with electrolytes are promising interventions for mitigating the effect of heat stress on kidney health outcomes. The results confirm that occupational heat stress influences kidney health for U.S. agricultural workers. The mechanisms of this relationship have not been fully elucidated. More studies exploring heat protection interventions are needed. Full article

Monitoring optically-active water quality (OAWQ) parameters faces key challenges, primarily due to limited in situ measurements and the restricted availability of high-resolution multispectral remote sensing imagery. While deep learning has shown promise for OAWQ estimation, existing approaches such as GeoTile2Vec, which relies on geographic proximity, and SimCLR, a domain-agnostic contrastive learning method, fail to capture land cover-driven water quality patterns, limiting their generalizability. To address this, we present deep meta-connectivity representation (DMCR), which integrates multispectral remote sensing imagery with limited in situ measurements to estimate OAWQ parameters. Our approach constructs meta-feature vectors from land cover images to represent the water quality characteristics of each multispectral remote sensing image tile. We introduce the meta-connectivity concept to quantify the OAWQ similarity between different tiles. Building on this concept, we design a contrastive self-supervised learning framework that uses sets of quadruple tiles extracted from Sentinel-2 imagery based on their meta-connectivity to learn DMCR vectors. After the core neural network is trained, we apply a random forest model to estimate parameters such as chlorophyll-a (Chl-a) and turbidity using matched in situ measurements and DMCR vectors across time and space. We evaluate DMCR on Lake Erie and Lake Ontario, generating a series of Chl-a and turbidity distribution maps. Performance is assessed using the $R^2$ and $RMSE$ metrics. Results show that meta-connectivity more effectively captures water quality similarities between tiles than widely utilized geographic proximity approaches such as those used in GeoTile2Vec. Furthermore, DMCR outperforms baseline models such as SimCLR with randomly cropped tiles. The resulting distribution maps align well with known factors influencing Chl-a and turbidity levels, confirming the method’s reliability. Overall, DMCR demonstrates strong potential for large-scale OAWQ estimation and contributes to improved monitoring of inland water bodies with limited in situ measurements through meta-connectivity-informed deep learning. The temporal-spatial water quality maps can support large-scale inland water monitoring, early warning of harmful algal blooms. Full article

Synthetic Aperture Radar (SAR) technology has the characteristics of all-day and all-weather functionality; accordingly, it is not affected by rainy weather, overcoming the limitations of optical remote sensing, and it provides irreplaceable technical support for efficient water body extraction. To address the issues of low accuracy and unstable results in water body extraction from Sentinel-1 SAR images using a single method, a water body extraction method fusing the Sentinel-1 dual-polarized water index and random forest is proposed. This novel method enhances water extraction accuracy by integrating the results of two different algorithms, reducing the biases associated with single-method water body extraction. Taking Dalu Lake, Yinfu Reservoir, and Huashan Reservoir as the study areas, water body information was extracted from SAR images using the dual-polarized water body index, the random forest method, and the fusion method. Taking the normalized difference water body index extraction results obtained via Sentinel-2 optical images as a reference, the accuracy of different water body extraction methods when used with SAR images was quantitatively evaluated. The experimental results show that, compared with the dual-polarized water body index and the random forest method, the fusion method, on average, increased overall water body extraction accuracy and Kappa coefficients by 3.9% and 8.2%, respectively, in the Dalu Lake experimental area; by 1.8% and 3.5%, respectively, in the Yinfu Reservoir experimental area; and by 4.1% and 8.1%, respectively, in the Huashan Reservoir experimental area. Therefore, the fusion method of the dual-polarized water index and random forest effectively improves the accuracy and reliability of water body extraction from SAR images. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Chrysanthemum × morifolium (Ramat) Hemsl. (Hangbaiju), which has been widely consumed as a herbal tea for over 3000 years, is renowned for its biosafety and diverse bioactivities. This study investigates the impact of polyphenol-rich Hangbaiju extracts (HE) on high-fat diet-induced obesity in mice. HE contains phenolic acids and flavonoids with anti-obesity properties, such as apigenin, luteolin-7-glucoside, apigenin-7-O-glucoside, kaempferol 3-(6″-acetylglucoside), etc. To establish the obesity model, mice were randomly assigned into four groups (n = 8 per group) and administered with either HE or water for 42 days under high-fat or low-fat dietary conditions. Administration of low (LH) and high (HH) doses of HE both significantly suppressed body weight growth (by 16.28% and 16.24%, respectively) and adipose tissue enlargement in obese mice. HE significantly improved the serum lipid profiles, mainly manifested as decreased levels of triglycerides (28.19% in LH and 19.59% in HH) and increased levels of high-density lipoprotein cholesterol (44.34% in LH and 54.88% in HH), and further attenuated liver lipid deposition. Furthermore, HE significantly decreased the Firmicutes/Bacteroidetes ratio 0.23-fold (LH) and 0.12-fold (HH), indicating an improvement in the microecological balance of the gut. HE administration also elevated the relative abundance of beneficial bacteria (e.g., Allobaculum, norank_f__Muribaculaceae), while suppressing harmful pathogenic proliferation (e.g., Dubosiella, Romboutsia). In conclusion, HE ameliorates obesity and hyperlipidemia through modulating lipid metabolism and restoring the balance of intestinal microecology, thus being promising for obesity therapy. Full article

The contamination of water bodies by industrial dyes poses a significant environmental challenge on a global scale. Conventional wastewater treatment methods often suffer from limitations related to high cost, limited efficiency, and potential secondary environmental impacts. Recent advances in photocatalytic technologies have highlighted the potential of metal sulfide-based photocatalysts, particularly those synthesized through environmentally friendly, plant-mediated approaches, as promising alternatives for efficient and sustainable dye degradation. However, despite their promising potential, metal sulfide photocatalysts often suffer from limitations such as photocorrosion, low stability under irradiation, and rapid recombination of charge carriers, which restrict their long-term applicability. In light of these challenges, this review provides a comprehensive examination of the physicochemical characteristics, synthetic strategies, and photocatalytic applications of metal sulfides. Particular emphasis is placed on green synthesis routes employing plant-derived extracts, which offer environmentally benign and sustainable alternatives to conventional methods. Moreover, the review elucidates various modification approaches, most notably, the formation of heterostructures, as viable strategies to enhance photocatalytic efficiency and mitigate the aforementioned drawbacks. The green synthesis of metal sulfides, aligned with the principles of green chemistry, offers a promising route toward the development of sustainable and environmentally friendly water treatment technologies. Full article