Search Results (8,907)

Cereal leaf beetle (CLB, Oulema melanopus L.) is an important pest that damages cereals. Insecticide use against CLB could be reduced with targeted treatments. Our aims were to develop a methodology to map CLB damage on cereal fields using remote sensing. We investigated the suitability of four vegetation indices (VIs: the Visible Atmospherically Resistance Index (VARI), the Green Chromatic Coordinate (GCC), the Green Leaf Index (GLI), and the Normalized Green–Red Difference Index (NGRDI)) derived from RGB images (drone (UAV) imagery). Study sites were located in different regions of Hungary in 2024. Images were taken at different phenological stages of cereals. Suitability of VIs was analyzed with ANOVA and MANOVA. Machine learning models were developed to classify damaged field sections with random forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms. Results show that VARI, GCC, GLI, and NGRDI contain complementary features for early detection of CLB damage. Difference in sample points’ VI from field median is advantageous for the LGBM algorithm (F1_damaged = 0.64–0.72), while the best RF models were obtained with more features (F1_damaged = 0.66). Random test data splits had optimistic results (overall accuracy: RF = 0.63–0.80, LightGBM = 0.63–0.79) compared to spatially controlled test splits (overall accuracy: RF = 0.53–0.70, LightGBM = 0.53–0.62). Full article

Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne’s multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet’s scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. Full article

Cutibacterium acnes (C. acnes) has emerged as a potential contributor to prostate cancer (PCa) pathogenesis, yet the mechanistic basis remains unclear. This review explores the prevalence, persistence and mechanistic impact of C. acnes within the prostate to help decipher the functional consequence and diagnostic value of a C. acnes infection in this setting. We examine the evidence supporting C. acnes colonisation of both premalignant and malignant tissue, and critically evaluate how prostate tumour physiology, particularly hypoxia and low pH, may facilitate microbial persistence. Emerging data suggest that C. acnes modulates inflammatory and immune pathways, influencing macrophage activation, cytokine production, and the regulation of immune checkpoints. Additionally, we discuss studies demonstrating its involvement in DNA damage, host cell metabolism, and extracellular matrix remodelling. The identification of C. acnes in urinary and gut microbiomes, alongside the presence of its genomic DNA in extracellular vesicles in circulation indicate broad diagnostic potential. While discrepancies in methodology have hampered a consensus, recent genomic and functional studies provide new avenues to distinguish contamination from true pathogenicity. Ultimately, future research exploring whether C. acnes is a biomarker, bystander, or bona fide driver of PCa, and its potential role in personalised diagnostics are crucial to advance the field and unravel the predictive and therapeutic value of C. acnes. Clarifying this relationship will advance our understanding of microbiome-cancer dynamics and could help inform innovative early detection and screening strategies that improve patient care. Full article

Postharvest losses from potato tuber moth severely constrain seed quality in Andean smallholder systems. This study evaluated four locally available repellent plants—Ambrosia peruviana, Eucalyptus globulus, Artemisia absinthium, and Minthostachys mollis—applied as dried leaves layered within seed bags of INIA 302 ‘Amarilis’ under farmer-like storage at two highland sites in Cajamarca, Peru (Huaytorco, 3350 m; Samaday, 2750 m), over 187 days. Within each site, a Completely Randomized Design with three bag-level replicates per treatment was used, and damage was assessed after 187 days as incidence of attacked tubers, internal damage severity and live larval counts. Endpoint data were analyzed separately by site using Kruskal–Wallis tests followed by Dunn’s post hoc test with Šidák correction (α = 0.05). Across both sites, all botanicals significantly reduced damage severity and live larval counts relative to the untreated control. At the warmer, lower site, A. absinthium and M. verticillata achieved large effect sizes, with severity and larval numbers reduced by roughly 80–90% compared with the control, while at the cooler, higher site, larvae were not detected in any botanical treatment. These findings indicate that simple layering of dried leaves from locally available plants, particularly wormwood and muña, can substantially mitigate S. tangolias damage in highland seed potato stores and represents a promising, low-cost complement to integrated pest management, although multi-season and dose-response studies are still needed to confirm and refine this approach. Full article

Goose astrovirus 2 (GAstV-2) infection leads to visceral gout and swollen kidneys in goslings, causing a 5–50% mortality rate and significant economic losses for goose flocks. While most studies on the virus’s pathological damage have focused on the kidneys, few reports have examined the effects of this fecal-oral pathogen on the digestive system. This study investigated GAstV-2 localization, cellular targets, and its impact on intestinal structure and homeostasis in orally infected goslings. Twenty 1-day-old goslings were randomly assigned to the infected and control groups. Clinical signs, organ lesions, viral distribution, histopathology, and alterations in intestinal cell populations, cytokine expression, and signaling pathways were assessed at 7 days post-infection. GAstV-2 was detected in the duodenum, jejunum, ileum, cecum, and rectum, with the highest viral load in duodenal crypt cells. Infection induced crypt cell necrosis, reduced villus height, decreased villus-to-crypt ratio, and lowered numbers of goblet cells and Lgr5+ intestinal stem cells. In contrast, Paneth cell abundance, Bmi1+ stem cells, and tight junction-related gene expression increased. Inhibition of stem cell differentiation into goblet cells was observed, mediated by modulation of the Notch signaling pathway. Proinflammatory cytokines, including IL-1β, IL-6, IL-8, IL-22, and TNF-α, were markedly upregulated, indicating a strong inflammatory response. These results demonstrate that GAstV-2 preferentially targets duodenal crypt cells, disrupts epithelial renewal, and impairs mucosal barrier function, while triggering compensatory regenerative and immune mechanisms. This study provides new insights into the intestinal pathogenesis of GAstV-2 and identifies potential targets for interventions to mitigate intestinal injury and economic losses in gosling production. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

Real-time detection of fresh corn ear height can provide a basis for dynamic adjustment of harvester header parameters, reducing mechanical damage and improving harvest quality. This study proposes a corn ear height detection model (CEHD). A YOLO-HAMDF network is developed for ear recognition, in which the core modules—TBDA, GLSA, and AQE—respectively suppress background interference, enhance contextual perception, and optimize bounding-box scoring. Depth information is incorporated to filter non-target regions and improve system robustness. In addition, a DI-DeepSORT module is designed for ear tracking, where DBC-Net and IDA-Kalman, respectively, enhance the discriminability of ReID features and enable independent-dimension adaptive noise modeling with smoothed positional updates. Experimental results demonstrate that the proposed CEHD model achieves a mean absolute error (MAE) of only 3.21 ± 0.05 cm under field conditions, indicating strong stability and practical applicability. In summary, this study presents a stable and reliable corn ear height detection system, achieves real-time monitoring of ear height, and provides data support for the dynamic adjustment of header parameters in fresh corn harvesters. Full article

The two-spotted cotton leafhopper, Amrasca biguttula (Ishida) (Hemiptera: Cicadellidae), is an invasive species native to the Indian subcontinent and an emerging pest of cotton, okra, eggplant, and hibiscus (Hibiscus syriacus; Malvaceae) in the southeastern United States. Its presence in ornamental nurseries has not been previously documented, posing a serious threat to hibiscus. This study aimed to evaluate the incidence of feeding damage and the potential spread of the pest across plant species within a nursery; thus, a study was conducted in a Georgia (USA) wholesale nursery in 2025. We used leaf samples to determine establishment, and leaf discoloration to categorize damage. We used yellow sticky cards to detect the presence of adults. Results showed that the life stages of A. biguttula were more common in the upper canopy than in the middle and lower levels. Both leafhopper numbers and exuviae were higher on the ‘Bali’ cultivar of H. syriacus compared to ‘Dark Lavender Chiffon’. No stages were found on neighboring species, including abelia, vitex, and rose. Yellow sticky card captures confirmed that adults were present on hibiscus cultivars. Feeding injury on H. syriacus was characterized by yellowing at the margins that spread inward and puckering of young leaves. Feeding damage ratings were significantly higher on ‘Bali’ than on ‘Dark Lavender Chiffon’, and adult captures were positively linked to damage severity. This is the first report of A. biguttula infestation and related injury on hibiscus in a U.S. wholesale nursery. Full article

In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire root-cause inspections to determine whether these fires are external or internal infrastructure fires. However, obtaining results is a complex process. In addition, the situation has been hampered by the lack of sufficient digital forensics and relevant programs. Apart from electrical devices, multi-sockets are among the main fire instigators. In this study, we aim to verify the feasibility of utilizing YOLO-based deep-learning object detection models for fire-cause inspection systems for multi-sockets. Particularly, we have created a novel image dataset of multi-socket fire causes with 3300 images categorized into the three classes of socket, both burnt-in and burnt-out. This data was used to train various models, including YOLOv4-csp, YOLOv5n, YOLOR-csp, YOLOv6, and YOLOv7-Tiny. In addition, we have proposed an improved YOLOv5n-SE by adding a squeeze-and-excitation network (SE) into the backbone of the conventional YOLOv5 network and deploying it into a two-stage detector framework with a first stage of socket detection and a second stage of burnt-in/burnt-out classification. From the experiment, the performance of these models was evaluated, revealing that our work outperforms other models, with an accuracy of 91.3% mAP@0.5. Also, the improved YOLOv5-SE model was deployed in a web browser application. Full article

Background/Objectives: To explore the protective effects of 7-Ketolithocholic acid (7-KLCA) against renal fibrosis and its mechanism, focusing on its interaction with farnesoid X receptor (FXR). Methods: In vitro, TGF-β-induced fibrosis in HK-2/NRK-49F cells and LPS-induced inflammation in HK-2 cells were detected by CCK-8, Western blot, and qPCR. In vivo, unilateral ureteral obstruction (UUO) and adenine (Ade)-induced mouse models were treated with a low/high-dose 7-KLCA or losartan. Renal injury was evaluated via H&E/Masson staining, serum creatinine (SCR), and blood urea nitrogen (BUN) levels. The 7-KLCA-FXR interaction was verified by molecular docking, CETSA, and DARTS. FXR downstream genes and related proteins were measured by WB and qPCR. Results: 7-KLCA inhibited the expression of fibrotic proteins (fibronectin, collagen-I) and reduced the LPS-induced release of inflammatory factors (IL-1β, IL-6). In mice, it alleviated renal swelling, collagen deposition, and tubular damage, while lowering serum SCR and BUN levels. Mechanistically, 7-KLCA stably bound to the FXR ligand-binding domain, enhanced its thermal stability and degradation resistance. It upregulated FXR and its downstream genes SHP and FGF15, thereby inhibiting the activation of TGF-β/Smad and Wnt/β-catenin pathways. Conclusions: This is the first study to clarify the molecular mechanism through which 7-KLCA targets FXR and dually suppresses the key pro-fibrotic pathways TGF-β/Smad and Wnt/β-catenin, thereby exerting anti-renal fibrosis effects. Full article

Diabetes mellitus represents one of the main health challenges worldwide, characterized by hyperglycemia and long-term serious microvascular and macrovascular complications. Marine organisms are a promising reservoir of bioactive metabolites for developing effective antidiabetic therapies with fewer side effects. The sea urchin Tripneustes gratilla (T. gratilla) is widely distributed in the Red Sea, with limited reports of its pharmacological activities and chemical characterization. In this study, a nanosuspension formulation of T. gratilla extract (T. gratilla-NS) was developed to enhance the bioavailability of its bioactive constituents. This study investigated the antidiabetic potential of T. gratilla extract through an integrated approach encompassing chemical profiling of the extract, assessment of its alcoholic extract for in vitro inhibitory effects on α-amylase and α-glucosidase, and in vivo evaluation of T. gratilla-NS in an alloxan-induced diabetic rat model. We found that the alcoholic extract showed potent inhibitory action toward α-amylase with IC₅₀ 5.31 ± 0.05 µg/mL and moderate inhibitory activity toward α-glucosidase with IC₅₀ 21.36 ± 0.06 µg/mL. T. gratilla-NS significantly increased insulin levels, reduced blood glucose levels, and restored pancreatic damage. Furthermore, it enhanced the levels of superoxide dismutase and total antioxidant capacity with a concomitant decrease in malondialdehyde concentration in pancreatic tissue. The observed activities could be attributed to a wide array of diverse compounds, terpenes, mainly sesquiterpenes, diterpenes, steroids, and polyunsaturated fatty acids detected by GC-MS, compounds with a phenolic nucleus equal to 54.26 ± 1.27 mg. GAE/g of extract. This research highlights the dual role of T. gratilla-NS in combating diabetes and subsequently attenuating its associated complications. Full article

The Tepetitlán reservoir is affected by untreated domestic wastewater, agricultural runoff, and livestock-related discharges, posing risks to aquatic ecosystems. This study assessed its water quality and evaluated toxic effects on adult zebrafish (Danio rerio). Water samples from six sites (A–F) were analyzed for physicochemical parameters and the presence of metals and pharmaceuticals, quantified at concentrations in the µg L⁻¹ range. While most physicochemical parameters complied with Mexican regulations, true color exceeded established limits, and the presence of contaminants indicated environmental deterioration. Zebrafish were exposed to water from each site for 12, 24, 48, 72, and 96 h. Oxidative stress (OS) biomarkers were measured in the brain, gill, gut, and liver at all time points, and gene expression of antioxidant, detoxification, and apoptosis-related genes was assessed at 96 h in these organs. Significant OS was detected across sites, with increased lipid peroxidation, protein carbonylation, and hydroperoxide levels. Although antioxidant enzymes were activated, their response did not fully counteract oxidative damage. Gene expression analysis revealed upregulation of stress- and apoptosis-related genes. These findings demonstrate that the Tepetitlán reservoir contains pollutant mixtures capable of inducing oxidative and molecular stress responses in zebrafish, underlining potential ecological risks and the need for mitigation efforts. Full article

Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle contamination, strong overload or operation without sufficient oil supply may be harmful to the bearings. In these cases, damage can occur quickly and with little warning time. Such spontaneous failure leads to turbine downtime and cost-intensive repair work on the wind turbine drive train. Thus, reliable load and condition monitoring systems, which allow the detection of critical operating states before damage occurs, would be beneficial. For journal bearings in wind turbine gearboxes, no commercially available monitoring system exists to date. The existing studies on journal bearing condition monitoring are limited to experiments on component test rigs or small gearboxes, and their transferability to full-size systems has yet to be proven. This work presents the results of a system test with an 850 kW wind turbine gearbox equipped with planetary journal bearings and a novel condition monitoring system based on the measurement of surface acoustic waves. First, the journal bearing design, including the sensor setup, is explained. Second, the test campaign layout is presented. The gearbox is tested under load conditions specific to wind turbines, and the condition monitoring signals are examined in detail. An algorithm based on a machine learning model is presented for evaluating the monitoring signals and predicting the friction state of the bearings. Finally, the practical feasibility and quality of the monitoring approach for planetary journal bearings presented in this work is discussed. Full article

Accurate mapping of hurricane-induced damage is essential for guiding rapid disaster response and long-term recovery planning. This study evaluates the Three-Dimensional Multi-Attributes, Multiscale, Multi-Cloud (3DMASC) framework for semantic classification of pre- and post-hurricane Light Detection and Ranging (LiDAR) data, using Mexico Beach, Florida, as a case study following Hurricane Michael. The goal was to assess the framework’s ability to classify stable landscape features and detect damage-specific classes in a highly complex post-disaster environment. Bitemporal topo-bathymetric LiDAR datasets from 2017 (pre-event) and 2018 (post-event) were processed to extract more than 80 geometric, radiometric, and echo-based features at multiple spatial scales. A Random Forest classifier was trained on a 2.37 km² pre-hurricane area (Zone A) and evaluated on an independent 0.95 km² post-hurricane area (Zone B). Pre-hurricane classification achieved an overall accuracy of 0.9711, with stable classes such as ground, water, and buildings achieving precision and recall exceeding 0.95. Post-hurricane classification maintained similar accuracy; however, damage-related classes exhibited lower performance, with debris reaching an F1-score of 0.77, damaged buildings 0.58, and vehicles recording a recall of only 0.13. These results indicate that the workflow is effective for rapid mapping of persistent structures, with additional refinements needed for detailed damage classification. Misclassifications were concentrated along class boundaries and in structurally ambiguous areas, consistent with known LiDAR limitations in disaster contexts. These results demonstrate the robustness and spatial transferability of the 3DMASC–Random Forest approach for disaster mapping. Integrating multispectral data, improving small-object representation, and incorporating automated debris volume estimation could further enhance classification reliability, enabling faster, more informed post-disaster decision-making. By enabling rapid, accurate damage mapping, this approach supports sustainable disaster recovery, resource-efficient debris management, and resilience planning in hurricane-prone regions. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovial inflammation and progressive joint damage. Although serum biomarkers such as rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) are widely used, blood-based testing is invasive. Saliva has emerged as a noninvasive diagnostic medium with clinical potential. This study aimed to evaluate the potential utility of salivary calprotectin and anti-CCP antibodies for discriminating patients with RA from healthy controls. Methods: Saliva samples were collected from 58 RA patients and 50 healthy controls. Salivary calprotectin and anti-CCP antibody levels were quantified using enzyme-linked immunosorbent assay. The diagnostic performance was evaluated using receiver operating characteristic curve analysis and logistic regression models that incorporated both biomarkers and clinical variables. Results: Patients with RA exhibited significantly higher salivary calprotectin and anti-CCP levels than controls (both p < 0.001). Calprotectin showed high sensitivity (79.31%), whereas anti-CCP displayed high specificity (84.00%). Salivary calprotectin was associated with disease duration and joint damage, while anti-CCP correlated with the erythrocyte sedimentation rate, RF, and serum anti-CCP. A multivariate model combining salivary biomarkers with clinical factors indicated an excellent diagnostic discrimination. Conclusions: Salivary calprotectin and anti-CCP antibodies show potential as complementary noninvasive biomarkers for distinguishing patients with established RA from healthy controls. However, as saliva samples were not collected at the time of initial diagnosis, these findings primarily support disease discrimination rather than early detection. Further prospective studies involving newly diagnosed and at-risk populations are required to clarify their role in early diagnosis, monitoring, and clinical implementation. Full article