Search Results (5,335)

Agriculture is Ireland’s largest sector with agri-food exports amounting to EUR 15.2B in 2021. However, agriculture is also Ireland’s largest contributor to GHGs, accounting for 37.4% of emissions in 2020. Developing indigenous renewable energy sources is a national objective towards reducing GHG emissions. The National Policy Statement on the Bioeconomy of Ireland advises a cascading principle of biomass use, where higher-value applications are derived from biomass before energy generation. This research quantifies and characterises biomass wastes at farms, food production, and forestry settings in counties Monaghan and Tipperary, Ireland. Value chains, along with Sankey diagrams, are presented, which identify biomass that can be exploited for valorisation and show their fates in industry/environment. The quantity of biomass wastes available for valorisation under Business as Usual (BAU) vs. Best-Case Scenario (BCS) models is presented. BCS assumes a co-operative system to increase the feedstock available for valorisation. In Monaghan, 73 t of biomass waste vs. 240 t are available for valorisation under Scenario A vs. Scenario B, respectively. In contrast, in Tipperary, a 7-fold increase in biomass waste is achieved, comparing Scenario A (126 t) against Scenario B (905 t). This highlights the importance of engaging local stakeholders to build co-operative models for biomass valorisation. Not only is this environmentally beneficial, but also socially and economically advantageous. Creating indigenous fertiliser and energy sources is important for the island of Ireland, not only in meeting market demand, but also in reducing greenhouse gas (GHG) emissions and achieving emission reduction targets. Full article

Machine perfusion technologies have redefined the landscape of organ preservation by enabling not just static cold storage, but graft optimization and assessment with the opportunity for additional therapeutic interventions. Preservation solutions, traditionally developed for static cold storage, are now being adapted for use in dynamic perfusion platforms. The optimal composition for machine perfusion remains unclear as we shift to creating biologically intelligent platforms tailored to mitigate ischemia–reperfusion injury. This review presents a mechanistic framework for understanding organ preservation through the lens of shared vulnerabilities, particularly: mitochondrial dysfunction, endothelial barrier breakdown, and the activation of inflammatory cascades. We discuss the evolution of classical preservation solutions, the rationale for redox-targeted and endothelial-stabilizing additives, and the promise of modular approaches adaptable to both static cold storage and machine perfusion. By integrating recent preclinical insights, systems biology, and emerging clinical trials, we outline the path toward unified, precision-preservation strategies capable of expanding the donor pool and improving transplant outcomes. Full article

Dental caries, a prevalent biofilm-mediated chronic disease, causes enamel demineralization, pulp infection, and systemic complications. Dental plaque biofilm is the initiating factor for the occurrence and development of caries. Streptococcus mutans is an opportunistic pathogen linked to the structure and ecology of dental plaque biofilms. The molecular mechanism of S. mutans during biofilm ontogeny in driving cariogenesis has been extensively elucidated. Here, we observed that asiatic acid is a potent biofilm disruptor that selectively dismantles S. mutans biofilm architectures, prompting us to investigate its mechanism. The minimum biofilm inhibition concentration (MBIC) of asiatic acid on S. mutans was 62.5 μM, but the MBIC level did not substantially impede planktonic growth. Using the static active-attachment model, it was demonstrated that asiatic acid significantly reduced biofilm biomass (p < 0.001) and extracellular polysaccharides (EPS) content (p < 0.001), while concurrently diminishing acid production (p = 0.017) and metabolic activity (p = 0.014). Confocal and scanning electron microscopy further confirmed structural disintegration, including bacterial detachment and reduced biofilm thickness. Transcriptome analysis of S. mutans biofilm treated with asiatic acid revealed 454 differentially expressed genes (adjusted p < 0.05, |log₂FC| ≥ 1). Notably, genes related to the CiaRH two-component system (ciaR, ciaH), a central regulatory hub for biofilm maturation and acid tolerance. This disruption initiates a downstream cascade, causing a coordinated downregulation of critical gene clusters essential for virulence and pathogenesis, including stress response (htrA, clpP, groEL, dnaK), and the glucan-binding protein gene (gbpC) essential for biofilm structural integrity. These findings provide the first mechanistic evidence linking asiatic acid to transcriptional reprogramming in S. mutans biofilm, offering a novel ecological strategy for caries prevention by targeting key regulatory pathways. Full article

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

Monoclonal antibodies (mAbs) have revolutionized the treatment landscape for neurological diseases, providing targeted, mechanism-based therapies for conditions ranging from autoimmune demyelinating disorders to neurodegenerative diseases. In multiple sclerosis (MS), mAbs against CD20, CD52, and α4-integrins offer disease-modifying efficacy by altering immune responses, depleting B cells, or blocking leukocyte migration into the central nervous system (CNS). Similarly, novel agents under investigation, such as frexalimab and foralumab, modulate T and B cell interactions and regulatory immunity. In neuromyelitis optica spectrum disorder (NMOSD), mAbs targeting IL-6, the complement cascade, and B cell lineage have demonstrated significant clinical benefit in preventing relapses and disability. In Alzheimer’s disease (AD), several anti-amyloid mAbs have gained regulatory approval. Anti-tau and anti-α-synuclein antibodies, though promising, have shown limited efficacy to date in AD and parkinson’s disease (PD), respectively. The evolving armamentarium of mAbs reflects a paradigm shift toward personalized neuroimmunology and neurodegeneration-targeted treatments, based on ongoing clarification of molecular and neuroinflammatory mechanisms. In this context, the present review summarizes current evidence on mAbs used in CNS disorders, with an emphasis on their pathophysiological targets, molecular mechanisms, clinical efficacy, and safety. Full article

This study aims to elucidate the active components and underlying molecular mechanisms by which the ethanol-water extract of Cinnamomum migao H.W. Li (MG-EWE) inhibits cardiac fibroblast (CF) transdifferentiation and IL-6 production, providing insights into its anti-myocardial fibrosis effects. The chemical composition of MG-EWE was characterized using UPLC-Q-TOF-MS. Network pharmacology analysis identified active constituents and their mechanisms in inhibiting IL-6 production in CFs. An isoproterenol (ISO)-induced rat CF model was established to evaluate the effects of MG-EWE and its main monomers, Laurolitsine and Hecogenin, on cell proliferation, migration, collagen metabolism, IL-6 production, and key proteins in the ADRB2/JNK signaling pathway. A total of 173 compounds were identified in MG-EWE, with 14 core constituents regulating IL-6 synthesis via 16 key targets, including ADRB2 and MAPK9. Gene Ontology enrichment indicated that MG-EWE affects phosphorylation and the JNK signaling cascade. Molecular docking showed strong binding affinities between Laurolitsine, Hecogenin, and their targets ADRB2 and JNK. Experimentally, MG-EWE, Laurolitsine, and Hecogenin significantly inhibited ISO-induced CF proliferation, migration, and hydroxyproline synthesis, as well as the expression of p-ADRB2, p-JNK, p-c-Jun, and IL-6. MG-EWE inhibits CF transdifferentiation and IL-6 production via the ADRB2/JNK/c-Jun signaling axis, mediated by its constituents Laurolitsine and Hecogenin, highlighting its potential for drug development targeting myocardial fibrosis. Full article

In recent years, the proliferation of forged videos, known as deepfakes, has escalated significantly, primarily due to advancements in technologies such as Generative Adversarial Networks (GANs), diffusion models, and Vision Language Models (VLMs). These deepfakes present substantial risks, threatening political stability, facilitating celebrity impersonation, and enabling tampering with evidence. As the sophistication of deepfake technology increases, detecting these manipulated videos becomes increasingly challenging. Most of the existing deepfake detection methods use Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Vision Transformers (ViTs), achieving strong accuracy but exhibiting high computational demands. This highlights the need for a lightweight yet effective pipeline for real-time and resource-limited scenarios. This study introduces a lightweight deep learning model for deepfake detection in order to address this emerging threat. The model incorporates three integral components: MobileNet for feature extraction, a Convolutional Block Attention Module (CBAM) for feature enhancement, and a Gated Recurrent Unit (GRU) for temporal analysis. Additionally, a pre-trained Multi-Task Cascaded Convolutional Network (MTCNN) is utilized for face detection and cropping. The model is evaluated using the Deepfake Detection Challenge (DFDC) and Celeb-DF v2 datasets, demonstrating impressive performance, with 98.2% accuracy and a 99.0% F1-score on Celeb-DF v2 and 95.0% accuracy and a 97.2% F1-score on DFDC, achieving a commendable balance between simplicity and effectiveness. Full article

In the pursuit of sustainable development, optimizing water resources management while maintaining ecological balance is crucial. This study introduces a Chaos-enhanced Harris Hawks Optimizer (CEHHO) aimed at optimizing natural flow patterns in cascade reservoirs. First, an ecological scheduling model considering ensuring guaranteed output is established based on the similarity of ecological flows. Subsequently, the CEHHO algorithm is proposed, which uses tilted skew chaos mapping for population initialization, improving the quality of the initial population. In the exploration phase, an adaptive strategy enhances the efficiency of group search algorithms, enabling effective navigation of the complex solution space. A random difference mutation strategy, combined with the Q-learning algorithm, mitigates premature convergence and maintains algorithmic diversity. Comparative analysis with the existing technology under different typical hydrological frequency shows that the search accuracy and convergence efficiency of the proposed method are significantly improved. Under the guaranteed output limit of 1000 MW, the proposed method enhances the optimal, median, mean, and worst values by 293.92, 493.23, 422.14, and 381.15, respectively, compared to the HHO. Furthermore, the results of the multi-purpose guaranteed output scenario highlight the superior detection and exploitation capabilities of this algorithm. These findings highlight the great potential of the proposed method for practical engineering applications, providing a reliable tool for optimizing water resources management while maintaining ecological balance. Full article

The study of aquatic biodiversity in the context of ecosystem sustainability is of urgent research importance, with several existing knowledge gaps. Among the under-studied groups are silica-scaled chrysophytes. Their cells are covered with silica scales and bristles/spines, the species-specific structure of which can be distinguished only by electron microscopy. In June and August 2024, samples were collected from a broad aquatic system comprising the southern part of Lake Baikal and a cascade of four reservoirs formed after the construction of hydroelectric dams on the Angara River flowing from Lake Baikal. Using electron microscopy, we identified 45 species of silica-scaled chrysophytes in phytoplankton in 2024, and the overall checklist was expanded to 57, accounting for interannual differences. Clear differences in species composition and richness were observed both between seasons and among reservoirs. Approximately a quarter of the recorded species were heterotrophs, which do not contribute to primary production, whereas 44% were phototrophs and 31% mixotrophs, both groups contributing to the Si cycle and to primary production. Continuous monitoring of reservoirs is essential for understanding the processes shaping silica-scaled chrysophytes diversity and may serve as an additional criterion for assessing the sustainability and transformation of freshwater ecosystems. Full article

Acute kidney injury (AKI) is a frequent and severe complication in trauma patients, affecting up to 28% of intensive care unit (ICU) admissions and contributing significantly to morbidity, mortality, and long-term renal impairment. Trauma-related AKI (TRAKI) arises from diverse mechanisms, including hemorrhagic shock, ischemia–reperfusion injury, systemic inflammation, rhabdomyolysis, nephrotoxicity, and complex organ crosstalk involving the brain, lungs, and abdomen. Pathophysiologically, TRAKI involves early disruption of the glomerular filtration barrier, tubular epithelial injury, and renal microvascular dysfunction. Inflammatory cascades, oxidative stress, immune thrombosis, and maladaptive repair mechanisms mediate these injuries. Trauma-related rhabdomyolysis and exposure to contrast agents or nephrotoxic drugs further exacerbate renal stress, particularly in patients with pre-existing comorbidities. Traditional markers such as serum creatinine (sCr) are late indicators of kidney damage and lack specificity. Emerging structural and stress response biomarkers—such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), C-C motif chemokine ligand 14 (CCL14), Dickkopf-3 (DKK3), and the U.S. Food and Drug Administration (FDA)-approved tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein 7 (TIMP-2 × IGFBP-7)—allow earlier detection of subclinical AKI and better predict progression and the need for renal replacement therapy. Together, functional indices like urinary sodium and fractional potassium excretion reflect early microcirculatory stress and add clinical value. In parallel, risk stratification tools, including the Renal Angina Index (RAI), the McMahon score, and the Haines model, enable the early identification of high-risk patients and help tailor nephroprotective strategies. Together, these biomarkers and risk models shift from passive AKI recognition to proactive, personalized management. A new paradigm that integrates biomarker-guided diagnostics and dynamic clinical scoring into trauma care promises to reduce AKI burden and improve renal outcomes in this critically ill population. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

The blood–brain barrier (BBB) is critical for central nervous system homeostasis, yet it is highly vulnerable to viral insults. While acute coronavirus infections are known to impair BBB integrity, the long-term impact of persistent infection on brain endothelial cells remains poorly understood. Using an in vitro BBB model, we examined the effects of a 12-week infection with the neurotropic murine coronavirus MHV-JHM. Structural and functional changes were assessed via fluorescein isothiocyanate (FITC)-dextran permeability assay, confocal imaging of mitochondria, actin cytoskeleton, reactive oxygen species (ROS), scanning electron microscopy (SEM) and RT-qPCR for viral RNA level. Long-term infection induced progressive mitochondrial fragmentation and sustained ROS overproduction. Permeability to 70 kDa dextran increased significantly at 48 h post-infection and exceeded control levels threefold by 168 h. SEM revealed gradual endothelial surface roughening, blebbing, and eventual monolayer collapse with extensive intercellular gaps by week 12. Our study demonstrates that long-term MHV-JHM infection profoundly alters brain endothelial cell structure and function, triggering a cascade of changes that culminate in the disintegration of the BBB model. Full article

In this paper, climate evolution is revisited in terms of the theory of dynamical systems, which has been successfully used in predictions of catastrophic events such as avalanches, landslides, or economy and civilization collapses. Such tipping events are announced by warning signs, named “pre-critical fluctuations” or “critical softening”, allowing a tipping date estimate through well-known equations. In the case of climate, the warning signs are extreme events of increasing amplitudes. We show that in such a context, numerical simulations can hardly predict incoming tipping points, due to a divergence in computational time at the singularity. Based on the dynamical systems theory, a recent publication from Copenhagen University shows that the Atlantic Meridional Oceanic Circulation is likely to collapse well before the end of the century, triggering switchover cascades, eventually culminating in global climate tipping. Paleoclimatic studies also show that tipping events occurred in the past, particularly during the PETM period 56 Myrs ago. If this was to happen now, average global temperatures might reach an unbearable level, with a deadline much closer than expected. This extreme emergency has major consequences on the implementation times of sustainability policies and in energy production, mobility, agriculture, housing, etc., that absolutely must be operational on time. Full article

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better frequency response, and an extra degree of freedom in tuning by using non-integer order terms. Although it has the benefits, there are three primary drawbacks: (i) it is not real-time adaptable, (ii) it is hard to choose appropriate initial gain values, and (iii) it is sensitive to big disturbances and parameter changes. A new control framework is suggested to address these problems. First, a Reinforcement Learning (RL) approach based on Deep Deterministic Policy Gradient (DDPG) is presented to optimize the FOPID gains online so that the controller can adjust itself continuously to the variations in the system. Second, Snake Optimization (SO) algorithm is used in fine-tuning of the FOPID parameters at the initial stages to guarantee stable convergence. Lastly, cascade control structure is adopted, where FOPID controllers are used in the inner (current) and outer (speed) loops. This construction adds robustness to the system as a whole and minimizes the effect of disturbances on the performance. In addition, the cascade design also allows more coordinated and smooth control actions thus reducing stress on the power electronic switches, which reduces switching losses and the overall efficiency of the drive system. The suggested RL-enhanced cascade FOPID controller is verified by Hardware-in-the-Loop (HIL) testing, which shows better performance in the aspects of speed regulation, robustness, and adaptability to realistic conditions of operation in EV applications. Full article