Search Results (9,097)

Microorganisms, as abundant biological resources, offer significant potential in the development of selenium-enrichment technologies. Selenium-enriched microorganisms not only absorb, reduce, and accumulate selenium efficiently but also produce various selenium compounds without relying on synthetic chemical processes. In particular, nano-selenium produced by these microorganisms during cultivation has garnered attention due to its unique physicochemical properties and biological activity, making it a promising raw material for functional foods and pharmaceutical products. This paper reviews selenium-enriched microorganisms, focusing on their classification, selenium metabolism, and transformation mechanisms. It explores how selenium is absorbed, reduced, and transformed within microbial cells, analyzing the biochemical processes by which inorganic selenium is converted into organic and nano-selenium forms. Finally, the broad applications of selenium-enriched microbial products in food, medicine, and agriculture are explored, including their roles in selenium-rich foods, nano-selenium materials, and disease prevention and treatment. Full article

With the continuous increase in the proportion of distributed energy output in the distribution network and the limited equipment on the management side of the active distribution network, it is very important to give full play to the regulating role of the dispatchable potential of large-scale electric vehicles for the economic operation of the distribution network. To deal with this issue, this paper proposes an optimal dispatching model of the distribution network considering the combination of the dispatchable potential of electric vehicle clusters and demand response. Firstly, the active distribution network dispatching model with the demand response is introduced, and the equipment involved in the active distribution network dispatching is modeled. Secondly, the bidirectional long short-term memory network algorithm is used to process the historical data of electric vehicles to reduce the uncertainty of the model. Then, the shared energy-storage characteristics based on the dispatchable potential of electric vehicle clusters are fully explored and the effect of peak shaving and valley filling after the demand response is fully explored. This approach significantly reduces the network loss and operating cost of the active distribution network. Finally, the modified IEEE-33 bus test system is utilized for test analysis in the case analysis, and the test results show that the established active distribution network model can reduce the early construction cost of the system’s energy-storage equipment, improve the energy-utilization efficiency, and realize the economic operation of the active distribution network. Full article

This study aimed to assess whether dietary supplementation with probiotics could alleviate intestinal injury in lipopolysaccharide (LPS)-challenged piglets. Healthy weaned piglets were randomly allocated to four individual groups (n = 6): (1) a control group; (2) an LPS group; (3) an LPS + Lactobacillus group; and (4) an LPS + Bacillus group. The control and LPS groups received a basal diet, while the probiotic groups were provided with the same basal diet supplemented with 6 × 10⁶ cfu/g of Lactobacillus casei (L. casei) or a combination of Bacillus subtilis (B. subtilis) and Bacillus licheniformis (B. licheniformis) at a dosage of 3 × 10⁶ cfu/g, respectively. On day 31 of the trial, overnight-fasted piglets were killed following the administration of either LPS or 0.9% NaCl solution. Blood samples and intestinal tissues were obtained for further analysis several hours later. The results indicate that dietary supplementation with probiotics significantly exhibited health-promoting effects compared with the control group and effectively reduced LPS-induced histomorphological damage to the small intestine, impairments in barrier function, and dysregulated immune responses via modulation of enzyme activity and the expression of relevant genes, such as nuclear factor-kappa B (NF-κB), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), claudin-1, nuclear-associatedantigenki-67 (Ki-67), and β-defensins-1 (pBD-1). Collectively, these results suggest that dietary supplementation with probiotics could alleviate LPS-induced intestinal injury by enhancing the immunity and anti-inflammatory responses in piglets. Our research provides a theoretical basis for the rational application of probiotics in the future. Full article

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy (SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional sensitivity, time resolution, and high frame-rate imaging capabilities. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. This study introduces a pioneering use of a large-format SPAD camera for DSCA. By harnessing the camera’s high temporal resolution and photon-detection efficiency, we significantly enhance the accuracy and robustness of speckle contrast measurements. Our experimental results demonstrate the system’s remarkable ability to capture rapid temporal variations over a broad field of view, enabling detailed spatiotemporal analysis. Through simulations, phantom experiments, and in vivo studies, we validated the proposed approach’s potential for cerebral blood flow and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, setting the stage for breakthroughs in optical imaging. Full article

Salient object detection is a challenging task in the field of computer vision. The graph-based model has attracted lots of research attention and achieved remarkable progress in this task, which constructs graphs to formulate the intrinsic structure of any image. Nevertheless, the existing graph-based salient object detection methods still have certain limitations and face two major challenges: (1) Previous graphs are constructed by the Gaussian kernel, but they are often corrupted by original noise. (2) They fail to capture common representations and complementary diversity of multi-view features. Both of these degrade saliency performance. In this paper, we propose a novel method, called multi-scale pure graphs with multi-view subspace clustering for salient object detection. Its main contribution is a new, two-stage graph, constructed and constrained by multi-view subspace clustering with sparsity and low rank. One of the advantages is that the multi-scale pure graphs upgrade the saliency performance from the propagation of noise in the graph matrix. Another advantage is that the multi-scale pure graphs exploit consistency and complementary information among multi-view features, which can effectively boost the capability of the graphs. In addition, to verify the impact of the symmetry of the multi-scale pure graphs on the salient object detection performance, we compared the proposed two-stage graphs, which included cases considering the multi-scale pure graphs and those not considering the multi-scale pure graphs. The experimental results were derived using several RGB benchmark datasets and several state-of-the-art algorithms for comparison. The results demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of multiple standard evaluation metrics. This paper reveals that multi-view subspace clustering is beneficial in promoting graph-based saliency detection tasks. Full article

Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts. Full article

The fractional PDE-ODE coupled system with Neumann boundary condition is considered in this paper. We design a boundary state feedback controller using the Backstepping method to stabilize the considered system. According to operator semigroup theory, we obtain a unique solution of the investigated system. Based on this, by using the fractional Lyapunov scheme, we prove that the system is Mittag-Leffler stable. Full article

This study was conducted to investigate the effects of puffed jujube powder (PJP) supplementation in the diet on the slaughter characteristics, growth performance, meat quality, and serum antioxidant capacity of Hainan Black (HB) goats. Twenty-four healthy male HB goats, three months old with an initial body weight of 15.12 ± 3.67 kg, were randomly divided into three groups: the 10% PJP group (basal diet plus 10% PJP); the 20% PJP group (basal diet plus 20% PJP); and the control group (basal diet only). After a 10-day adaptation period, a feeding trial was carried out for 90 days in an ad libitum diet environment. The results show that the final body weight of the 20% PJP group was markedly higher (p < 0.05) than that of the control group (22.58 ± 0.94 kg vs. 20.45 ± 1.01 kg). The average daily gain of the 20% PJP group was 83.44 ± 1.78 g/d, which was substantially greater (p < 0.05) than the 59.22 ± 2.13 g/d of the control group. The feed intake of the 20% PJP group was 713.10 ± 4.54 g/d, notably higher (p < 0.05) than the 498.20 ± 4.33 g/d of the control group. In terms of slaughter characteristics, the carcass weight of the 20% PJP group was 13.99 ± 1.22 kg, considerably heavier (p < 0.05) than the 11.79 ± 1.38 kg of the control group. The muscle weight of the 20% PJP group was 11.43 ± 1.42 kg, distinctly greater (p < 0.05) than the 9.59 ± 1.99 kg of the control group. The slaughter rate of the 20% PJP group was 42.41%, showing a notable increase (p < 0.05) compared with the 37.42% of the control group, and the net meat rate of the 20% PJP group was 34.65%, with a significant rise (p < 0.05) compared with the 30.43% of the control group. Regarding serum antioxidant capacity and meat quality, the activities of serum antioxidant enzymes, superoxide dismutase (SOD) and catalase (CAT), were conspicuously increased (p < 0.05) in the 20% PJP group. The meat shear force of the 20% PJP group was decreased by 12.9%, and the cooking loss was improved by 8.9% in comparison with the control group. In conclusion, the supplementation of 20% PJP in the diet was demonstrated to enhance the growth performance, improve the meat quality, and boost the antioxidant status of HB goats, thus presenting a feasible strategy for optimizing tropical goat production systems. Full article

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

Introduction: Osteoarthritis (OA), a leading cause of disability among the elderly, is characterized by progressive joint tissue destruction. Fucoidan, a sulfated polysaccharide with known anti-inflammatory and antioxidant properties, has been investigated for its potential to protect against interleukin-1 beta (IL-1β)-induced articular tissue damage. Methods: Human primary chondrocytes and synovial fibroblasts were pre-treated with 100 μg/mL fucoidan before stimulation with 1 ng/mL of IL-1β. The protective effects of fucoidan were assessed by measuring oxidative stress markers and catabolic enzyme levels. These in vitro findings were corroborated using a rat anterior cruciate ligament transection-induced OA model. To explore the underlying mechanisms, particularly the interaction between microRNAs (miRs) and heme oxygenase-1 (HO-1), five candidate miRs were identified in silico and experimentally validated. Luciferase reporter assays were used to confirm direct interactions. Results: Fucoidan exhibited protective effects against IL-1β-induced oxidative stress and catabolic processes in both chondrocytes and synovial fibroblasts, consistent with in vivo observations. Fucoidan treatment restored HO-1 expression while reducing inducible nitric oxide synthase and matrix metalloproteinase levels in IL-1β-stimulated cells. Notably, this study revealed that fucoidan modulates the miR-22/HO-1 pathway, a previously uncharacterized mechanism in OA. Specifically, miR-22 was upregulated by IL-1β and subsequently attenuated by fucoidan. Luciferase reporter assays confirmed a direct interaction between miR-22 and HO-1. Conclusion: The results demonstrate that fucoidan mitigates OA-related oxidative stress in chondrocytes and synovial fibroblasts through the novel modulation of the miR-22/HO-1 axis. The miR-22/HO-1 pathway represents a crucial therapeutic target for OA, and fucoidan may offer a promising therapeutic intervention. Full article

This paper presents a novel four-dimensional (4D) chaotic system exhibiting parametric symmetry breaking and multistability. Through equilibrium stability analysis, attractor reconstruction, Lyapunov Exponent spectra (LEs), and bifurcation diagrams, we reveal a continuous transition from symmetric period attractors to asymmetric chaotic states and rich dynamical behaviors. Additionally, considering the potential of this system in practical applications, a feedback control simulation circuit is designed and implemented to ensure its stability and effectiveness under real-world conditions. Finally, among various control strategies, this paper proposes an innovative Fixed-Time Sliding Mode Synchronization (FTSMS) strategy, determines its synchronization convergence time, and provides an important theoretical foundation for the practical application of the system. Full article

Object detection in remote sensing imagery is critical in environmental monitoring, urban planning, and land resource management. However, the task remains challenging due to significant scale variations, arbitrary object orientations, and complex background clutter. To address these issues, we propose a novel orientation module (SOAM Block) that jointly models object scale and directional features while exploiting geometric symmetry inherent in many remote sensing targets. The SOAM Block is constructed upon a lightweight and efficient Adaptive Multi-Scale (AMS) Module, which utilizes a symmetric arrangement of parallel depth-wise convolutional branches with varied kernel sizes to extract fine-grained multi-scale features without dilation, thereby preserving local context and enhancing scale adaptability. In addition, a Strip-based Context Attention (SCA) mechanism is introduced to model long-range spatial dependencies, leveraging horizontal and vertical 1D strip convolutions in a directionally symmetric fashion. This design captures spatial correlations between distant regions and reinforces semantic consistency in cluttered scenes. Importantly, this work is the first to explicitly analyze the coupling between object scale and orientation in remote sensing imagery. The proposed method addresses the limitations of fixed receptive fields in capturing symmetric directional cues of large-scale objects. Extensive experiments are conducted on two widely used benchmarks—DOTA and HRSC2016—both of which exhibit significant scale variations and orientation diversity. Results demonstrate that our approach achieves superior detection accuracy with fewer parameters and lower computational overhead compared to state-of-the-art methods. The proposed SOAM Block thus offers a robust, scalable, and symmetry-aware solution for high-precision object detection in complex aerial scenes. Full article

Slow-release nitrogen fertilizers enhance crop production and reduce environmental pollution, but their slow nitrogen release may cause insufficient nitrogen supply in the early stages of crop growth. Mixed nitrogen fertilization (MNF), combining slow-release nitrogen fertilizer with urea, is an effective way to increase yield and income and improve nitrogen fertilizer efficiency. This study used urea alone (Urea) and slow-release nitrogen fertilizer alone (C/SRF) as controls and employed meta-analysis and a random forest model to assess MNF effects on crop yield and nitrogen partial factor productivity (PFPN), and to identify key influencing factors. Results showed that compared with urea, MNF increased crop yield by 7.42% and PFPN by 8.20%, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 20 °C, and elevations of 750–1050 m; in soils with a pH of 5.5–6.5, where 150–240 kg·ha⁻¹ nitrogen with 25–35% content and an 80–100 day release period was applied, and the blending ratio was ≥0.3; and when planting rapeseed, maize, and cotton for 1–2 years. The top three influencing factors were crop type, nitrogen rate, and soil pH. Compared with C/SRF, MNF increased crop yield by 2.44% and had a non-significant increase in PFPN, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 5 °C, average annual precipitation ≤ 400 mm, and elevations of 300–900 m; in sandy soils with pH > 7.5, where 150–270 kg·ha⁻¹ nitrogen with 25–30% content and a 40–80 day release period was applied, and the blending ratio was 0.4–0.7; and when planting potatoes and rapeseed for 3 years. The top three influencing factors were nitrogen rate, crop type, and average annual precipitation. In conclusion, MNF should comprehensively consider crops, regions, soil, and management. This study provides a scientific basis for optimizing slow-release nitrogen fertilizers and promoting the large-scale application of MNF in farmland. Full article

Perfluoroalkyl substances (PFASs) possess immunosuppressive properties. However, their association with rheumatoid arthritis (RA) risk remains inconclusive across epidemiological studies. This study integrates population-based and mechanistic evidence to clarify the relationship between PFAS exposure and RA. We analyzed 8743 U.S. adults from the NHANES (2005–2018), assessing individual and mixed exposures to PFOA, PFOS, PFNA, and PFHxS using multivariable logistic regression, Bayesian kernel machine regression, quantile g-computation, and weighted quantile sum models. Network toxicology and molecular docking were utilized to identify core targets mediating immune disruption. The results showed that elevated PFOA (OR = 1.63, 95% CI: 1.41–1.89), PFOS (OR = 1.41, 1.25–1.58), and PFNA (OR = 1.40, 1.20–1.63) levels significantly increased RA risk. Mixture analyses indicated a positive joint effect (WQS OR = 1.06, 1.02–1.10; qgcomp OR = 1.26, 1.16–1.38), with PFOA as the primary contributor. Stratified analyses revealed stronger effects in females (PFOA Q4 OR = 3.75, 2.36–5.97) and older adults (≥60 years). Core targets included EGFR, SRC, TP53, and CTNNB1. PFAS mixtures increase RA risk, dominated by PFOA and modulated by sex/age. These findings help reconcile prior contradictions by identifying key molecular targets and vulnerable subpopulations, supporting regulatory attention to PFAS mixture exposure. Full article

Diabetes mellitus (DM) is one of the leading causes of death and disability worldwide and its prevalence continues to rise. Chronic hyperglycemia exposes patients to severe complications. Among these, diabetic vascular lesions are the most destructive. Their primary driver is the synergistic interaction between hyperglycemia-induced oxidative stress and chronic inflammation. This review systematically elucidates how multiple pathological pathways—namely, metabolic dysregulation, mitochondrial dysfunction, endoplasmic reticulum stress, and epigenetic reprogramming—cooperate to drive oxidative stress and inflammatory cascades. Confronting this complex pathological network, natural products, unlike conventional single-target synthetic drugs, exert multi-target synergistic effects, simultaneously modulating several key pathogenic networks. This enables the restoration of redox homeostasis and the suppression of inflammatory responses, thereby improving vascular function and delaying both microvascular and macrovascular disease progression. However, the clinical translation of natural products still faces multiple challenges and requires comprehensive mechanistic studies and rigorous validation to fully realize their therapeutic potential. Full article