Search Results (3,229)

Nitrogen (N) and potassium (K) are essential macronutrients for plants whose functions and interactions profoundly influence plant physiological metabolism, environmental adaptation, and agricultural production efficiency. This review summarizes research advances in plant N and K nutrition and their interaction mechanisms, elucidating the key physiological functions of N and K individually and their respective absorption and transport mechanisms involving transporters such as NRTs and HAKs/KUPs. The review discusses the types of nutrient interactions (synergism and antagonism), with a primary focus on the physiological basis of N–K interactions and their interplay in root absorption and transport (e.g., K⁺-NO₃⁻ co-transport; NH₄⁺ inhibition of K⁺ uptake), photosynthesis (jointly optimizing CO₂ conductance, mesophyll conductance, and N allocation within photosynthetic machinery to enhance photosynthetic N use efficiency, PNUE), as well as sensing, signaling, co-regulation, and metabolism. This review emphasizes that N–K balance is crucial for improving crop yield and quality, enhancing fertilizer use efficiency (NUE/KUE), and reducing environmental pollution. Consequently, developing effective N–K management strategies based on these interaction mechanisms and implementing Balanced Fertilization Techniques (BFT) to optimize N–K ratios and application strategies in agricultural production represent vital pathways for ensuring food security, addressing resource constraints, and advancing green, low-carbon agriculture, including through coordinated management of greenhouse gas emissions. Full article

Melittin, a cytolytic peptide derived from honeybee venom, has demonstrated potent anticancer activity through mechanisms such as membrane disruption, apoptosis induction, and modulation of key signaling pathways. Melittin exerts its anticancer activity by interacting with key molecular targets, including downregulation of the PI3K/Akt and NF-κB signaling pathways, and by inducing mitochondrial apoptosis through reactive oxygen species generation and cytochrome c release. However, its clinical application is hindered by its systemic and hemolytic toxicity, rapid degradation in plasma, poor pharmacokinetics, and immunogenicity, necessitating the development of targeted delivery strategies to enable safe and effective treatment. Nanoparticle-based delivery systems have emerged as a promising strategy for overcoming these challenges, offering improved tumor targeting, reduced off-target effects, and enhanced stability. This review provides a comprehensive overview of the mechanisms through which melittin exerts its anticancer effects and evaluates the development of various melittin-loaded nanocarriers, including liposomes, polymeric nanoparticles, dendrimers, micelles, and inorganic systems. It also summarizes the preclinical evidence for melittin nanotherapy across a wide range of cancer types, highlighting both its cytotoxic and immunomodulatory effects. The potential of melittin nanoparticles to overcome multidrug resistance and synergize with chemotherapy, immunotherapy, photothermal therapy, and radiotherapy is discussed. Despite promising in vitro and in vivo findings, its clinical translation remains limited. Key barriers include toxicity, manufacturing scalability, regulatory approval, and the need for more extensive in vivo validation. A key future direction is the application of computational tools, such as physiologically based pharmacokinetic modeling and artificial-intelligence-based modeling, to streamline development and guide its clinical translation. Addressing these challenges through focused research and interdisciplinary collaboration will be essential to realizing the full therapeutic potential of melittin-based nanomedicines in oncology. Overall, this review synthesizes the findings from over 100 peer-reviewed studies published between 2008 and 2025, providing an up-to-date assessment of melittin-based nanomedicine strategies across diverse cancer types. Full article

The activation mechanism of the reproductive axis in Kazakh ewes during the non-breeding season was explored by supplementation with glycerol complex (7% glycerol + tyrosine + vitamin B9). The experiment divided 50 ewes into five groups (n = 10). After 90 days of intervention, it was found that significant changes in serum DL-carnitine, N-methyl-lysine and other differential metabolites were observed in the GLY-Tyr-B9 group (p < 0.05, “p < 0.05” means significant difference, “p < 0.01” means “highly significant difference”). The bile acid metabolic pathway was specifically activated (p < 0.01). The group had a 50% estrus rate, ovaries contained 3–5 immature follicles, and HE staining showed intact granulosa cell structure. Serum E2/P4 fluctuated cyclically (p < 0.01), FSH/LH pulse frequency increased (p < 0.01), peak Glu/INS appeared on day 60 (p < 0.05), and LEP was negatively correlated with body fat percentage (p < 0.01). Molecular mechanisms revealed: upregulation of hypothalamic kiss-1/GPR54 expression (p < 0.01) drove GnRH pulses; ovarian CYP11A1/LHR/VEGF synergistically promoted follicular development (p < 0.05); the HSL of subcutaneous fat was significantly increased (p < 0.05), suggesting involvement of lipolytic supply. Glycerol activates the reproductive axis through a dual pathway—L-carnitine-mediated elevation of mitochondrial β-oxidation efficacy synergizes with kisspeptin/GPR54 signalling enhancement to re-establish HPO axis rhythms. This study reveals the central role of metabolic reprogramming in regulating seasonal reproduction in ruminants. Full article

Introduction. Larvae of the insect Hermetia illucens can represent an alternative source for low-molecular-weight chitosan (CS) production compared with CS from crustaceans (CS_crustac), making it appealing in terms of pharmaceutical applications. Hence, the performances of CS_larvae and CS_crustac were compared herein by investigating the in vitro features of nanoparticles (NPs) made from each polysaccharide and administered with the antioxidant quercetin (QUE). Methods. X-ray diffraction and FT-IR spectroscopy enabled the identification of each type of CS. Following the ionic gelation technique and using sulfobutylether-β-cyclodextrin as a cross-linking agent, NPs were easily obtained. Results. Physicochemical data, release studies in PBS, and the evaluation of antioxidant effects via the 1,1-diphenyl-2-picrylhydrazyl (DPPH) test were studied for both CS_larvae and CS_crustac. QUE-loaded NP sizes ranged from 180 to 547 nm, and zeta potential values were between +7.5 and +39.3 mV. In vitro QUE release in PBS was faster from QUE-CS_larvae NPs than from CS_crustac, and high antioxidant activity—according to the DPPH test—was observed for all tested NP formulations. Discussion. The agar diffusion assay, referring to Escherichia coli and Micrococcus flavus, as well as the microdilution assay, showed the best performance as antimicrobial formulations in the case of QUE-CS_larvae NPs. QUE-CS_larvae NPs can represent a promising vehicle for QUE, releasing it in a sustained manner, and, relevantly, the synergism noticed between QUE and CS_larvae resulted in a final antimicrobial product. Conclusions. New perspectives for low-molecular-weight CS are disclosed by adopting renewable sources from insects instead of the commercial CS_crustac. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

Soil salinization severely restricts crop growth and presents a major challenge to global agriculture. In this study, a plant-growth-promoting rhizobacterium (PGPR) was isolated and identified as Proteus sp. through 16S rDNA analysis and was subsequently named Proteus sp. JHY1. Under salt stress, exogenous dopamine (DA) significantly enhanced the production of indole-3-acetic acid and ammonia by strain JHY1. Pot experiments revealed that both DA and JHY1 treatments effectively alleviated the adverse effects of 225 mM NaCl on rice, promoting biomass, plant height, and root length. More importantly, the combined application of DA-JHY1 showed a significant synergistic effect in mitigating salt stress. The treatment increased the chlorophyll content, net photosynthetic rate, osmotic regulators (proline, soluble sugars, and protein), and reduced lipid peroxidation. The treatment also increased soil nutrients (ammoniacal nitrogen and available phosphorus), enhanced soil enzyme activities (sucrase and alkaline phosphatase), stabilized the ion balance (K⁺/Na⁺), and modulated the soil rhizosphere microbial community by increasing beneficial bacteria, such as Actinobacteria and Firmicutes. This study provides the first evidence that the synergistic effect of DA and PGPR contributes to enhanced salt tolerance in rice, offering a novel strategy for alleviating the adverse effects of salt stress on plant growth. Full article

The hypothalamus–pituitary–ovarian (HPO) axis orchestrates reproductive functions through intricate neuroendocrine crosstalk. Here, we integrated single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics (ST) to decode the cellular heterogeneity and intercellular communication networks in the reproductive systems of pregnant Mongolian cattle. We retained a total of 6161 high-quality nuclei from the hypothalamus, 14,715 nuclei from the pituitary, and 26,072 nuclei from the ovary, providing a comprehensive cellular atlas across the HPO axis. In the hypothalamus, neurons exhibited synaptic and neuroendocrine specialization, with glutamatergic subtype Glut4 serving as a TGFβ signaling hub to regulate pituitary feedback, while GABAergic GABA1 dominated PRL signaling, likely adapting maternal behavior. Pituitary stem cells dynamically replenished endocrine populations via TGFβ, and lactotrophs formed a PRL–PRLR paracrine network with stem cells, synergizing mammary development. Ovarian luteal cells exhibited steroidogenic specialization and microenvironmental synergy: endothelial cells coregulated TGFβ-driven angiogenesis and immune tolerance, while luteal–stromal PRL–PRLR interactions amplified progesterone synthesis and nutrient support. Granulosa cells (GCs) displayed spatial-functional stratification, with steroidogenic GCs persisting across pseudotime as luteinization precursors, while atretic GCs underwent apoptosis. Spatial mapping revealed GCs’ annular follicular distribution, mediating oocyte–somatic crosstalk, and luteal–endothelial colocalization supporting vascularization. This study unveils pregnancy-specific HPO axis regulation, emphasizing multi-organ crosstalk through TGFβ/PRL pathways and stem cell-driven plasticity, offering insights into reproductive homeostasis and pathologies. Full article

Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

Polymicrobial biofilms involving fungal and bacterial species are increasingly recognized as contributors to persistent infections, particularly in the oral cavity. Candida albicans and Aggregatibacter actinomycetemcomitans are two commensals that can turn into opportunistic pathogens and are able to form robust biofilms. Objectives: This study aimed to assess the interaction dynamics between these two microorganisms and to evaluate their susceptibility to fluconazole and azithromycin in single- and mixed-species forms. Methods: Biofilm biomass was quantified using crystal violet assays, while biofilm cell viability was assessed through CFU enumeration (biofilm viability assay). To assess the resistance properties of single versus mixed-species coincubations, we applied the antimicrobial susceptibility test (AST) to each drug, and analysed spatial organization with confocal laser scanning microscopy, using PNA-FISH. Results: The results indicated that both species can coexist without significant mutual inhibition. However, a non-reciprocal synergism was also observed, whereby mixed-species biofilm conditions promoted the growth of A. actinomycetemcomitans, while C. albicans growth remained stable. As expected, antimicrobial tolerance was elevated in mixed cultures, likely due to enhanced extracellular matrix production and potential quorum-sensing interactions, contributing to increased resistance against azithromycin and fluconazole. Conclusions: This study provides novel insights into previously rarely explored interactions between C. albicans and A. actinomycetemcomitans. These findings underscore the importance of investigating interspecies interactions within polymicrobial biofilms, as understanding their mechanisms, such as quorum-sensing molecules and metabolic cooperation, can contribute to improved diagnostics and more effective targeted therapeutic strategies against polymicrobial infections. Full article

A global poly-crisis of climate change, biodiversity loss, dwindling natural resources, geopolitical instability, among other complex challenges, is on the rise. Societal transformations are therefore imminent, whether intended or unintended. The key question is how to steward and facilitate such changes where fragmentation and siloed ways of working persist. The concept of synergies and the notion of synergic action could help overcome fragmented efforts to steer transformative changes. However, there exists a critical research gap in understanding the conditions needed to enable synergic action. This paper thus explores how synergic action is currently undertaken and the key essentials needed to deliver synergic action. The study uses a case study of the Yorkshire food system transformation to learn from its exemplar practitioners. The study used semi-structured interviews and a thematic analysis process to reach our two key findings. First, we highlight the three types of synergic action: (1) Non-systemic synergic action, (2) Non-systemic synergic action with multiple outcomes, and (3) Systemic synergic action. Differentiating types of synergic action can help identify where synergic action is already underway and guide more explicit efforts towards transformative change. The second key finding is the five essentials for synergic action, which are (1) leadership for synergic action; (2) networking, partnerships, and collaborations; (3) care and understanding; (4) a systems approach; and (5) intentionality for synergic action. This study brings to the fore the importance of intentionality, without which the first four essentials are less likely to coalesce. This is important to inform the reflection and learning of practitioners of systemic change about how they are currently and could be working more synergistically in the future, driven by clear intentionality. Full article

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

In the rock–soil–biology–water ecosystem, rock weathering provides essential plant nutrients. However, its supply is insufficient for rising crop demands under population growth and climate change, while excessive fertilizer causes soil degradation and pollution. This study innovatively irrigated with carbonate rock leachates to enhance soil nutrient availability. A pot experiment with lettuce showed that irrigation significantly increased soil NO₃⁻-N (+102.20%), available K (+16.45%), available P (+17.95%), Ca (+6.04%), Mg (+11.65%), and Fe (+11.60%), and elevated the relative abundance of Firmicutes. Lettuce biomass per plant rose by 23.78%, with higher leaf minerals (P, K, Ca, and Mg) and antioxidants (carotenoids and ascorbic acid). A field experiment further confirmed improvement of soil nutrient availability and peanut yield. This carbonate rock leachate irrigation technique effectively enhances soil quality and crop productivity/quality, offering a sustainable approach for green agriculture. Full article

The Kuroshio Current’s flow velocity imposes exacting requirements on underwater vehicle propulsive systems. Ecological preservation necessitates low-noise propeller designs to mitigate operational disturbances. As technological evolution advances toward greater intelligence and system integration, intelligent unmanned systems are positioning themselves as a critical frontier in marine innovation. In recent years, the global research community has increased its efforts towards the development of high-maneuverability underwater vehicles. However, propeller design optimization ignores the key balance between acoustic performance and hydrodynamic efficiency, as well as the appropriate speed threshold for blade rotation. In order to solve this problem, the propeller design of the NACA 65A010 airfoil is optimized by using OpenProp v3.3.4 and XFlow 2022 software, aiming at innovating the propulsion system of shallow water agile submersibles. The study presents an integrated design framework combining lattice Boltzmann method (LBM) simulations synergized with fully Lagrangian-LES modeling, implementing rotational speed thresholds to detect cavitation inception, followed by advanced acoustic propagation analysis. Through rigorous comparative assessment of hydrodynamic metrics, we establish an optimization protocol for propeller selection tailored to littoral zone operational demands. Studies have shown that increasing the number of propeller blades can reduce the single-blade load and delay cavitation, but too many blades will aggravate the complexity of the flow field, resulting in reduced efficiency and noise rebound. It is concluded that the propeller with five blades, a diameter of 234 mm, and a speed of 500 RPM exhibits the best performance. Under these conditions, the water efficiency is 69.01%, and the noise is the lowest, which basically realizes the balance between hydrodynamic efficiency and acoustic performance. This paradigm-shifting research carries substantial implications for next-generation marine vehicles, particularly in optimizing operational stealth and energy efficiency through intelligent propulsion architecture. Full article