Search Results (1,150)

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

Tunnel point cloud semantic segmentation is a critical step in achieving refined perception and intelligent management of tunnel structures. Addressing common challenges including indistinct boundaries and fine-grained category discrimination, this paper proposes MFPNet, a multi-scale feature perception network specifically designed for tunnel scenarios. This approach employs kernel convolution to effectively model local point cloud geometries within continuous spaces. Building upon this foundation, an error-feedback-based local-global feature fusion mechanism is designed. Through bidirectional information exchange, higher-level semantic information compensates for and constrains lower-level geometric features, thereby mitigating information fragmentation across semantic hierarchies. Furthermore, an adaptive feature re-calibration and cross-scale contextual correlation mechanism is introduced to dynamically modulate multi-scale feature responses. This explicitly models contextual dependencies across scales, enabling collaborative aggregation and discriminative enhancement of multi-scale semantic information. Experimental results on tunnel point cloud datasets demonstrate that the proposed MFPNet has achieved significant improvements in both overall segmentation accuracy and category balance, with mIoU reaching 87.5%, which is 5.1% to 33.0% higher than mainstream methods such as PointNet++ and RandLA-Net, and the overall classification accuracy reaching 96.3%. These results validate the method’s efficacy in achieving high-precision three-dimensional semantic understanding within complex tunnel environments, providing robust technical support for tunnel digital twin and intelligent detection applications. Full article

Background/Objectives: Pseudomonas aeruginosa is one of the leading causes of ventilator-associated pneumonia (VAP) and ventilator-associated tracheobronchitis (VAT), with a worldwide spread of difficult-to-treat high-risk clones. This study aimed to investigate the virulence potential and to characterize phenotypic and genotypic antimicrobial resistance (AMR) in P. aeruginosa causing VAP/VAT in the Intensive Care Unit (ICU), University Hospital of Split, Croatia. Methods: The study included P. aeruginosa isolates obtained from ICU patients who met the criteria for VAP or VAT, between January 2023 and January 2024. Isolates were identified using MALDI-TOF MS and tested for antimicrobial susceptibility (AST). A subset of phenotypically multidrug-resistant (MDR) isolates was further analyzed using whole-genome sequencing (WGS) and multilocus sequence typing. Results: A high rate of resistance was detected to ceftazidime (23.4%), imipenem (39.6%), and meropenem (43.8%). WGS confirmed the presence of multiple AMR genes, including the bla_VIM-2 gene, whose genetic environment highlights a complex MDR locus integrating multiple AMR determinants and mobile genetic elements. All tested isolates possessed genes for class C (bla_PDC34, bla_PDC374 or bla_PDC16) and class D (bla_OXA-2, bla_OXA-10 or bla_OXA-50) β-lactamases, fosA, aph(3′)-IIb and crpP genes. Additionally, WGS analysis revealed the presence of numerous virulence genes including those for adherence (Type IV pili and Fap protein production), motility (such as flgF), biofilm formation (e.g., algE and mucE), quorum sensing (lasI, lasR, rhlI and rhlR), exotoxin (toxA and plcH) and exoenzyme activity (exoU, exoT, exoS, exoY, pcrV, hcp1 and lasA). The isolates belonged to four different sequence types: ST235, ST446, the high-risk ST253 and the widely distributed ST395. Phylogenomic comparison demonstrated that the isolates from this study do not originate from a single clonal source, but instead represent multiple globally distributed high-risk P. aeruginosa lineages introduced into the clinical setting. Conclusions: Due to the emergence of high-risk clones with broad AMR and strong virulence potential, ineffectiveness of standard empirical therapy may be anticipated, highlighting the urgent need for new therapeutic approaches (including those targeting major virulence factors). Full article

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts. Full article

Nitrite, as a widely present nitrogen oxide compound in nature, and is extensively distributed in production and daily life; precise and rapid detection of it is of great significance for ensuring human health. This study developed nitrogen-doped carbon dots (N-CDs) using malic acid and 3-diethylaminophenol as precursors by one-step hydrothermal treatment. The obtained N-CDs exhibited strong green fluorescence with a high quantum yield of 20.86%. More importantly, they served as a highly effective fluorescent probe for NO₂⁻ sensing, demonstrating a low detection limit of 28.33 μM and a wide linear response range of 400 to 1000 μM. The sensing mechanism was attributed to an electrostatic interaction-enhanced dynamic quenching process. Notably, the probe enabled dual-mode detection: a distinct color change from light pink to dark brown under daylight for visual semi-quantification, and quantitative fluorescence quenching. The N-CDs showed excellent selectivity over common interfering ions. Furthermore, their low cytotoxicity and good biocompatibility allowed for successful bioimaging of exogenous and endogenous NO₂⁻ fluctuations in live HeLa cells. This work presents a facile green strategy to synthesize multifunctional N-CDs that realized the sensitive, selective, and visual detection of NO₂⁻ in environmental and biological systems. Full article

By analyzing Food and Drug Administration (FDA) enforcement reports from 2004 to 2025, we can determine the incidence of microbial contamination in non-sterile and sterile drugs in the United States of America and, at the same time, compare the trends and patterns over a period of 21 years to determine the distribution and frequency of microbial contaminants. The most common microorganisms detected from 2019 to 2025 were the mold Aspergillus penicilloides, with 17 citations for sterile products, followed by 16 citations for non-sterile products of Burkholderia cepacia complex (BCC) bacteria. Analysis from the last 21 years revealed the dominant microbial contaminants belong to the BCC, reaching a maximum level between 2012 and 2019. Some of the previous microbial contaminants, such as Salmonella and Clostridium, decline in the 2019–2025 period, with no notifications issued. S. aureus and Pseudomonas contamination persisted through the years but at very low levels. Gram-negative bacteria contaminated non-sterile drugs more frequently than Gram-positive. A worrisome trend continued with unacceptable levels of enforcement reports not providing any information on the identity of the microbial contaminant. New species of Bacillus and Acetobacter nitrogenifigens were responsible for a significant increase in non-sterile drug recalls. The main driver for sterile product recalls over a 21-year period is the lack of assurance of sterility (LAS) where major failures in process design, control, and operational execution were not conducive to the control of microbial proliferation and destruction. Enforcement data analysis identified the problematic trends and patterns regarding microbial contamination of drugs, providing important information to optimize process control and provide a framework for optimizing risk mitigation. Although the 21-year landscape demonstrated that some microbial contaminants have been successfully mitigated, others remain resilient. The emergence of new contaminants highlights the evolving nature of microbial risk. The consistent problem with LAS is not only a major regulatory violation but also a potential catalyst for the next major healthcare-associated outbreak. Full article

Gold nanoparticles (Au-NP) are frequently used as model nanomaterials to study nanoparticle behavior in plants due to their analytical detectability and negligible natural background in plant tissues. However, the feasibility of visualizing the spatial distribution of foliar-applied Au-NP at low exposure levels using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remains insufficiently explored. In this study, commercially sourced Au-NP were applied to lentil leaves (Lens culinaris var. Beluga) at a low concentration of 0.5 mg·L⁻¹ using a controlled leaf submersion approach. Leaves were sampled at 1 h, 24 h, and 96 h post-exposure and analyzed by LA-ICP-MS imaging to assess time-dependent changes in gold-associated spatial signals, and to compare elemental distribution patterns with non-exposed controls. Untreated control leaves showed no detectable gold at any sampling time point, confirming negligible native Au background. In treated leaves, LA-ICP-MS imaging revealed an initially localized Au hotspot at 1 h, followed by progressive Au redistribution toward the leaf margins and petiole region by 24 h and 96 h. Gold signals persisted over the full 96 h period, indicating stable association of Au-NP with leaf tissue. Comparative elemental mapping of Ca, Mg, K, P, Fe, Zn, and Cu showed no persistent differences in spatial distribution patterns between treated and control leaves as detectable by LA-ICP-MS. This study demonstrates the feasibility of LA-ICP-MS imaging for visualizing the deposition and temporal spatial redistribution of low-dose foliar-applied nanoparticles in intact leaves. The results provide a methodological reference for future hypothesis-driven studies that apply nanoparticles under more controlled conditions, include increased replication, and combine multiple analytical techniques. Full article

This study investigated the effects of different additives on the fermentation quality and bacterial community of silage prepared from Giant Juncao grass (Cenchrus fungigraminus) grown in saline–alkali soil. Four treatments were compared: a control group (CK), wheat bran (WB), fermented Juncao grass juice (FJGJ), and a combined wheat bran + fermented Juncao grass juice treatment (WB + FJGJ). Dynamic changes in physicochemical characteristics—including dry matter (DM), pH, lactic acid (LA), acetic acid (AA), propionic acid (PA), and total volatile fatty acids (TVFA)—were monitored together with shifts in bacterial community structure. Quantitative results showed that FJGJ and WB + FJGJ significantly improved fermentation performance. Compared with the control, the WB + FJGJ treatment reduced the final pH to 3.61 (p < 0.05) and increased lactic acid concentration to 48 g/kg DM. Concentrations of acetic acid and TVFA were also higher in additive-treated silages than in the control. Redundancy analysis indicated that pH and lactic acid were the main environmental factors associated with changes in bacterial community composition, whereas ether extract and acetic acid showed weaker but detectable effects. Bacterial community profiling revealed that genera such as Secundilactobacillus and Lacticaseibacillus dominated in the additive-treated groups, and that the additives significantly altered microbial community structure compared with the control. Overall, the combined application of wheat bran and fermented Juncao grass juice improved the fermentation quality of Giant Juncao grass silage grown on saline–alkali soil and promoted a bacterial community dominated by beneficial lactic acid–producing taxa. Full article

Fermented forest litter (FFL) is a biofertilizer obtained by anaerobic fermentation of forest litter combined with agricultural by-products. Its production involves an initial one-month solid-state fermentation of oak litter mixed with whey, molasses and wheat bran, followed by a one-week submerged fermentation-called the “activation” phase-during which the solid FFL is fermented with sugarcane molasses diluted in water. This study aimed to evaluate the effects storage duration (6, 18 and 30 months), and temperature (ambient and 29 °C) on the activation phase. For this purpose, pH, sugar consumption and metabolite production dynamics were monitored. Under all experimental conditions, the pH dropped to values close to 3.5, sucrose was rapidly hydrolyzed, and glucose was preferentially consumed over fructose. Fructose was metabolized only after glucose was depleted, suggesting the involvement of fructophilic microorganisms. The time-course evolution of lactic acid (LA) concentration was adequately fitted by the Gompertz model (R² > 0.970). The highest LA_max concentration (6.30 g/L) and production rate (2.16 g/L·d) were obtained with FFL stored for 6 months. Acetic acid (AA) and ethanol were also detected reaching maxima values of 1.19 g/L and 0.96 g/L, respectively. Their profiles varied depending on the experimental conditions. Notably, the AA/LA ratio increased with the age of the FFL. Overall, sugar consumption and metabolite production were significantly slower at ambient temperature, than at 29 °C. These results contribute to a better understanding of the metabolic dynamics during FFL activation and highlight key parameters that should be considered to optimize future biofertilizer production processes. Full article

Total anomalous pulmonary venous connection (TAPVC) is one of the most severe congenital heart defects; however, prenatal diagnosis remains suboptimal. A normal fetal heart has a junction between the pulmonary venous (PV) and left atrium (LA). In contrast, no junctions are observed in patients with TAPVC. In the present study, we attempted to visualize and detect fetal PV-LA connections using artificial intelligence (AI) trained on the fetal cardiac ultrasound videos of 100 normal cases and six TAPVC cases. The PV-LA aggregate area was segmented using the following three-dimensional (3D) segmentation models: SegResNet, Swin UNETR, MedNeXt, and SegFormer3D. The Dice coefficient and 95% Hausdorff distance were used to evaluate segmentation performance. The mean values of the shortest PV-LA distance (PLD) and major axis angle (PLA) in each video were calculated. These methods demonstrated sufficient performance in visualizing and detecting the PV-LA connection. In terms of TAPVC screening performance, MedNeXt-PLD and SegResNet-PLA achieved mean area under the receiver operating characteristic curve values of 0.844 and 0.840, respectively. Overall, this study shows that our approach can support unskilled examiners in capturing the PV-LA connection and has the potential to improve the prenatal detection rate of TAPVC. Full article

The reliance on unstable hydrogen peroxide (H₂O₂) adversely affects the robustness and simplicity of chemiluminescence (CL)-based immunoassays. We report a novel external H₂O₂-free Fenton CL system integrated into a highly sensitive non-enzymatic immunoassay for the detection of SARS-CoV-2 nucleoprotein, utilizing cuprous–polyethylenimine–lipoic acid nanoflowers (Cu(I)-PEI-LA-Ab NF) as a non-enzymatic tag. The signaling polymer (PEI-LA) was synthesized via EDC/NHS coupling, which conjugated approximately 550 LA units to the PEI backbone. This polymer formed antibody-conjugated NF with various metal ions, and the Cu(I)-based variant was selected for its intense and sustained CL with luminol. The mechanism relies on an in situ Fenton reaction, in which dissolved oxygen is reduced by Cu(I) to H₂O₂, which reacts with oxidized Cu(II), producing hydroxyl radicals that oxidize luminol. Direct calibration of the SARS-CoV-2 nucleoprotein fixed on microplate wells demonstrated excellent linearity in the range of 0.01–3.13 ng/mL (LOD = 3 pg/mL). In a final competitive immunoassay format for samples spiked with the antigen, a decreasing CL signal that correlated with increasing antigen concentration was obtained in the range of 0.1–20.0 ng/mL, achieving excellent recoveries that were favorable compared with those of the sandwich ELISA kit, establishing this H₂O₂-independent platform as a powerful and robust tool for clinical diagnostics. Full article

Currently, the differences in the responses of the organic acid metabolism in rapeseed (Brassica napus L.) roots to saline and alkaline stresses are still unknown. To clarify the differences, different saline (100 (LS) and 200 (HS) mmol/L NaCl) and alkaline (20 (LA) and 40 (HA) mmol/L Na₂CO₃) treatments were applied to rapeseed. Then, targeted metabolomics was used to quantitatively analyze the changes in organic acid metabolism in the root system. The results showed that compared with the control group without stress (CK), 21, 18, 27, and 20 differentially accumulated organic acid metabolites were detected in the rapeseed roots under LS, HS, LA, and HA, respectively. In addition, 26, 6, 34, and 14 differentially accumulated organic acids were detected in the rapeseed root exudates under LS, HS, LA, and HA, respectively. Based on the activities of key enzymes related to the tricarboxylic acid cycle (TCA), antioxidant enzyme activities, organic acid metabolism, and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis in rapeseed roots, rapeseed mainly resisted saline and alkaline stresses by increasing organic acid synthesis and scavenging reactive oxygen species. Specifically, rapeseed resisted saline stress mainly by increasing the secretion of TCA cycle-related organic acids such as succinic acid, L-malic acid, fumaric acid, and cis-aconitic acid. In addition to secreting organic acids, rapeseed also resisted alkaline stress by increasing the secretion of phenolic acids such as 4-hydroxybenzoic acid, ferulic acid, and 4-coumaric acid. Notably, the number of secreted organic acid types and the increase in organic acid content under alkaline stress were higher than those under saline stress. The results of this study provide an important basis for the breeding of saline and alkaline stress-tolerant rapeseed varieties. Full article

Background/Objectives: Pediatric cancer survivors frequently experience neuromuscular sequelae related to chemotherapy-induced neurotoxicity. Agents such as vincristine, methotrexate, and platinum compounds can lead to persistent gait alterations and sensorimotor deficits that impair mobility and quality of life. This study aimed to objectively assess gait in pediatric cancer survivors after the completion of oncological pharmacological treatment to identify specific spatiotemporal, kinematic, and kinetic alterations and characterize neuromechanical patterns associated with neurotoxic exposure. Methods: A cross-sectional observational study was conducted including pediatric cancer survivors (6–18 years) who had completed chemotherapy and age- and sex-matched healthy controls. Gait was analyzed using a Vicon^®3D motion capture system, with reflective markers placed on standardized anatomical landmarks. Spatiotemporal, kinematic, and kinetic variables were compared between groups using parametric tests and statistical parametric mapping (SPM) with Holm–Bonferroni correction (α = 0.05). Results: Pediatric cancer survivors showed slower gait velocity (Mean Difference (MD) = 0.17, p = 0.018, Confidence Interval CI95% = 0.04; 0.4), shorter step (MD = 0.1, p = 0.015, CI95% = 0.01; 0.19) and stride length (MD = 0.17, p = 0.018, CI95% = 0.03; 0.31), as well as reduced single support time (MD = 0.1, p = 0.043, CI95% = 0.01; 0.19), along with significant alterations in pelvic, hip, knee, and ankle kinematics compared with controls. Increased pelvic elevation (MD = 0.92, p = 0.018, CI95% = 0.25; 1.58), reduced hip extension during stance (MD = −2.99, p = 0.039, CI95% = −5.19; −0.74), knee hyperextension in mid-stance (MD = −3.84, p < 0.001, CI95% = −6.18; −0.72), and limited ankle dorsiflexion (MAS MD = −4.04, p < 0.001, CI95% = −6.79; −0.86, LAS MD = −3.16, p < 0.001) and plantarflexor moments in terminal stance (MAS MD = −149.65, p = 0.018, CI95% = −259.35; −48.25, LAS MD = −191.81, p = 0.008, CI95% = −323.81; −57.31) were observed. Ground reaction force peaks during loading response (MAS MD = −16.86, p < 0.001, CI95% = −26.12; −0.72 LAS MD = −11.74, p = 0.001, CI95% = −19.68; −3.94) and foot-off (MAS MD = 10.38, p = 0.015, CI95% = 0.41; 20.53, LAS MD = 11.88, p = 0.01, CI95% = 3.15; 22.38) were also reduced. Conclusions: Children who have completed chemotherapy present measurable gait deviations reflecting persistent neuromechanical impairment, likely linked to chemotherapy-induced neurotoxicity and deconditioning. Instrumented gait analysis allows early detection of these alterations and may support the design of targeted rehabilitation strategies to optimize functional recovery and long-term quality of life in pediatric cancer survivors. Full article

Background and Objectives: Today, immunoassay methods are still widely used in the analysis of hormone tests. Due to the properties of the reagents used in immunoassay analyses and components other than the measured analyte, deviations in clinical results may occur. There are many factors that cause this condition called interference, and one of the most common of these is heterophile antibody (Ab). Puberty is a process that begins between the ages of 8 and 13 in girls and 9 and 14 in boys. Pulsatile luteinizing hormone (LH) release during sleep is the first hormonal change that indicates the approach of puberty. The reliability of the laboratory analysis result is important. In order to determine whether there is a risk of interference in the LH tests we analyzed in our laboratory, 48 serum samples of pediatric patients belonging to the pubertal age group were included in the study. Materials and Methods: In order to evaluate the suspicion of heterophile Ab interference, we measured the samples again by binding the antibodies and removing them from the matrix, as recommended in the Clinical and Laboratory Standards Institute (CLSI) I/LA30 guideline. For this, we used a heterophile blocking tube (HBT). We analyzed the samples with Beckman Coulter UniCel DxI 800 and Roche Cobas e601 immunoassay systems. We aliquoted the supernatants of the samples processed according to the HBT application protocol and measured them on both autoanalyzers. Results: We found a significant difference between the results of the samples measured before and after HBT pretreatment on the Beckman Coulter UniCel DxI 800 autoanalyzer (p = 0.01). LH values after HBT were higher than those before HBT: very high LH values were obtained in 4 patients, while the values showed increases ranging from 2 to 4.64-fold in 5 patients. There was no significant difference between the results evaluated before and after HBT pretreatment on the Roche Cobas e601 autoanalyzer (p = 0.27). Although there was a significant difference between the LH results of the HBT-untreated sera obtained in two different autoanalyzers (p < 0.001), we found that the LH measurements after HBT pretreatment did not create a statistically significant difference between the two devices (p = 0.76). Conclusions: We concluded that while HBTs were ineffective in detecting heterophile antibody interference in LH testing, the study underscores the complexity of interference in pediatric hormone assays and highlights the need for further investigation into alternative methods to ensure reliable test results in this age group. Full article

Early and reliable detection of bearing faults is essential for ensuring the safe and efficient operation of rotating electrical machines, especially under varying loads and non-stationary operating conditions. However, traditional diagnostic approaches struggle to maintain accuracy when signals are noisy, high-dimensional, or affected by multiple fault patterns. To address these issues, this work presents RNN-XBoostNet, a lightweight hybrid framework that combines the temporal-feature extraction capability of Recurrent Neural Networks (RNNs) with the robust classification strength of XGBoost. A new feature-selection strategy, CoLaR-FS (integrating correlation analysis, Lasso regularization, and recursive feature elimination), is introduced to reduce redundancy and retain only the most discriminative fault features. The proposed framework is evaluated using the widely known CWRU dataset and a newly developed induction-machine dataset containing ten fault categories, including six newly introduced real-world conditions. Experimental results show significant performance improvements: accuracy increased from 87.01% to 99.35% on the CWRU dataset and from 79.98% to 99.57% on the laboratory dataset. The combination of high accuracy, reduced complexity, and strong generalization demonstrates that RNN-XBoostNet, supported by CoLaR-FS, is a practical and effective solution for modern condition-based monitoring systems. Full article