Search Results (28,040)

Ilex paraguariensis (yerba mate), a culturally and economically important South American species, faces significant challenges in vitro, including contamination, phenolic oxidation, and low regeneration rates. Nanoparticles have recently emerged as promising tools to overcome such limitations. This study evaluated silver (AgNPs) and chitosan nanoparticles (ChNPs) in eight experiments using nodal, leaf, and internodal explants. Surface disinfection with 1% colloidal silver solution 20 ppm significantly reduced contamination (17.2% and 15%) while maintaining viability (62.1%). However, supplementation of culture media with AgNPs (4–75 mg·L⁻¹) or ChNPs (5–120 mg·L⁻¹) did not improve nodal segment responses. In leaf explants, 4 mg·L⁻¹ AgNPs proved most effective, reducing contamination and markedly decreasing callus oxidation from 63.3% to 10.0%. Callogenesis was enhanced when AgNPs were combined with growth regulators, with the highest induction at 6 mg·L⁻¹ AgNPs + zeatin (38.1%) and 4 mg·L⁻¹ AgNPs + BAP (42.9%). Conversely, in internodal segments, AgNPs combined with BAP completely inhibiting callus formation. The resulting calli exhibited compact and friable morphologies but no signs of somatic embryogenesis. Overall, the effectiveness of AgNPs depends on their formulation, explant type, and interaction with cytokinins. Optimization of nanoparticle formulation and hormonal balance remains essential to maximize efficacy while minimizing toxicity. Full article

The rare and rapidly progressive neurodegenerative disease multiple system atrophy (MSA) mainly affects the striatum and other subcortical brain regions. In this atypical Parkinsonian syndrome, the protein alpha-synuclein aggregates and misfolds in neurons as well as glial cells and is released in elevated amounts by hypoexcitable neurons. Mitochondrial dysregulation affects the biosynthesis of coenzyme Q10 and the activity of the respiratory chain, as shown in an induced pluripotent stem cell (iPSC) model. Proteome studies of cerebrospinal fluid and brain tissue from MSA patients yielded inconsistent results regarding possible protein changes due to small and combined groups of atypical Parkinsonian syndromes. In this study, we analysed the cellular proteome of MSA patient-derived striatal GABAergic medium spiny neurons. We observed 25 significantly upregulated and 16 significantly downregulated proteins in MSA cell lines compared to matched healthy controls. Various protein types involved in diverse molecular functions and cellular processes emphasise the multifaceted pathomechanisms of MSA. These data could contribute to the development of novel disease-modifying treatment strategies for MSA patients. Full article

Introduction: Myotonic dystrophies (DM) are progressive genetic disorders with multisystemic involvement, particularly affecting the muscular, respiratory, and neuropsychological systems. Myotonic dystrophy type 1 (DM1), or Steinert’s disease, may lead to severe respiratory complications, including sleep-disordered breathing and hypercapnia, often requiring noninvasive ventilation to manage respiratory failure. However, adherence to NIV remains a major challenge, often influenced by cognitive and psychological factors such as apathy and depression. This study aims to investigate the presence of depression and SDB in patients with DM1 initiating NIV, and to evaluate factors influencing adherence to ventilatory support. Materials and Methods: We selected 13 adult patients (≥18 years) with diagnosis of Steinert’s disease with respiratory impairment who needed to start respiratory support. Dysphagia was assessed in all patients at baseline by a videofluoroscopic swallow study. Beck’s Depression Inventory II was administered for measuring the severity of depression. The Montreal Cognitive Assessment was used as a screening tool to detect signs of neurocognitive disorders. We evaluated adherence to NIV. Results: The study population presented with sleep-disordered breathing, as indicated by a median apnea–hypopnea index (AHI) of 24 events per hour (IQR: 14.2–34.5) and an oxygen desaturation index (ODI) of 25 events per hour (IQR: 18–33). Adherence to NIV was obtained in seven patients. No difference in baseline lung function was observed. Adherent subjects had moderate hypercapnia at baseline; pCO2 was 52 vs. 49 mmHg. Non-adherent patients showed a higher prevalence of depression with a median BDI-II score of 18 vs. 6 in adherent patients. The findings highlight that psychological factors, especially depression, play a crucial role in adherence to NIV. Interestingly, depression was not linked to initial respiratory measurements but showed a significant association with nocturnal oxygen desaturation. This suggests that relying solely on clinical and respiratory assessments may not be adequate to predict or improve treatment adherence. Conclusions: Incorporating psychological evaluations and addressing mental health issues, such as depression, are essential steps to enhance NIV compliance and overall DM1 patient outcomes. A multidisciplinary approach combining respiratory and psycho-emotional interventions is crucial for effective disease management. Full article

Amid the intensification of demographic aging, home robots based on intelligent technology have shown great application potential in assisting the daily life of the elderly. This paper proposes a multimodal human–robot interaction system that integrates EEG signal analysis and visual perception, aiming to realize the perception ability of home robots on the intentions and environment of the elderly. Firstly, a channel selection strategy is employed to identify the most discriminative electrode channels based on Motor Imagery (MI) EEG signals; then, the signal representation ability is improved by combining Filter Bank co-Spatial Patterns (FBCSP), wavelet packet decomposition and nonlinear features, and one-to-many Support Vector Regression (SVR) is used to achieve four-class classification. Secondly, the YOLO v8 model is applied for identifying objects within indoor scenes. Subsequently, object confidence and spatial distribution are extracted, and scene recognition is performed using a Machine Learning technique. Finally, the EEG classification results are combined with the scene recognition results to establish the scene-intention correspondence, so as to realize the recognition of the intention-driven task types of the elderly in different home scenes. Performance evaluation reveals that the proposed method attains a recognition accuracy of 83.4%, which indicates that this method has good classification accuracy and practical application value in multimodal perception and human–robot collaborative interaction, and provides technical support for the development of smarter and more personalized home assistance robots. Full article

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption. Full article

The rapid and sensitive detection of regulatory elements within transgenic constructs of genetically modified organisms (GMOs) is essential for effective monitoring and control of their distribution. In this study, we present several innovative electrochemical biosensing platforms for the detection of regulatory sequences in genetically modified (GM) plants, combining the loop-mediated isothermal amplification (LAMP) method with electrodes functionalized by two-dimensional (2D) nanomaterials. The sensor design exploits the high surface area and excellent conductivity of reduced graphene oxide, Ti₃C₂T_x, and molybdenum disulfide (MoS₂) to enhance signal transduction. Furthermore, we used a “green synthesis” method for Ti₃C₂T_x preparation that eliminates the use of hazardous hydrofluoric acid (HF) and hydrochloric acid (HCl), providing a safer and more sustainable approach for nanomaterial production. Within this framework, the performance of various custom-fabricated electrodes, including laser-patterned gold leaf films, physical vapor deposition (PVD)-deposited gold electrodes, and screen-printed gold electrodes, is evaluated and compared with commercial screen-printed gold electrodes. Additionally, gold and carbon electrodes were electrochemically covered by gold nanoparticles (AuNPs), and their properties were compared. Several electrochemical methods were used during the DNA detection, and their importance and differences in excitation signal were highlighted. Electrochemical properties, sensitivity, selectivity, and reproducibility are characterized for each electrode type to assess the influence of fabrication methods and material composition on sensor performance. The developed biosensing systems exhibit high sensitivity, specificity, and rapid response, highlighting their potential as practical tools for on-site GMO screening and regulatory compliance monitoring. This work advances electrochemical nucleic acid detection by integrating environmentally-friendly nanomaterial synthesis with robust biosensing technology. Full article

Urban heat island conditions increase heat exposure and constrain safe outdoor activities. Urban forests can mitigate thermal loads; however, stand morphology can produce divergent microclimates. We aimed to quantify how stand type (open vs. closed), season (spring, summer, fall), and activity intensity (MET 1.0–6.0) jointly modulate thermal comfort and to identify season-specific optimal MET levels in an urban forest in Daejeon, Republic of Korea. We combined site-specific 3D canopy modeling with hourly Predicted Mean Vote (PMV) simulations driven by AMOS tower data (2023–2024). Comfort was defined as |PMV| ≤ 0.5. Analyses included seasonal means, Cliff’s delta, and generalized estimating equation logistic models to estimate the SITE × SEASON × MET interactions and predict comfort probabilities. Across most seasons and MET levels, C1 was more comfortable than C2. However, at MET 1.0 in summer, the pattern was reversed, which may reflect the canopy shading and associated decreases in mean radiant temperature (MRT) of C2. Comfort peaked at MET 2.0–3.0 and declined sharply at ≥4.5 MET. The three-way SITE × SEASON × MET interaction was significant (p < 0.001). The season-specific optimal MET values under our boundary conditions were 3.0 (spring), 2.0–2.5 (summer), and 3.0 (fall). These simulation-based PMV-centered findings represent model-informed tendencies. Nevertheless, they support actionable guidance: prioritize high-closure stands for low-intensity summer use, leverage open stands for low-to-moderate activities in spring and fall, and avoid high-intensity programs during warm periods. These results inform the programming and design of urban-forest healing and recreation by matching stand type and activity intensity to season to maximize comfortable hours. Full article

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development. Full article

Crumb rubber used in asphalt modification can generally improve the road performance of asphalt mixture pavement while offering substantial environmental and economic benefits. This study investigates the volatile organic compound emissions from crumb rubber-modified asphalt binders via gas chromatography–mass spectrometry, focusing on the effects of crumb rubber types (e.g., activated crumb rubber, non-activated crumb rubber), contents, and additives (warm-mix agents, deodorants, styrene–butadiene–styrene (SBS)). The analysis encompasses total volatile organic compound emissions, compositional variations, secondary organic aerosol and ozone formation potentials, and carcinogenic risks. Results indicate that non-activated crumb rubber increases volatile organic compound emissions initially, peaking at a 15% content (3.99 times higher than base asphalt), dominated by trichloroethylene. The surfactant-based warm-mix additive significantly reduces emissions by 73%, whereas deodorants exhibited limited efficacy. At equivalent contents, activated crumb rubber-modified asphalt emits more volatile organic compounds than non-activated crumb rubber-modified asphalt and leads to a higher ozone formation potential. Activated crumb rubber/SBS-modified asphalt blends reduce emissions by 69%–81% due to synergistic effects. In contrast, non-activated crumb rubber/SBS blends increase emissions, likely due to phase separation. All samples contain carcinogens, primarily trichloroethylene (20%–79%) and benzene (0.1%–9%). These findings underscore the critical importance of crumb rubber activation status and SBS addition in controlling volatile organic compound diffusion. The activated crumb rubber/SBS combination achieves a synergistic reduction exceeding the sum of individual effects (“1 + 1 > 2”). These findings provide valuable insights for designing eco-friendly asphalt. Full article

Focusing on the discovery of new $\delta$ Scuti and $\gamma$ Doradus stars, we analyzed the Transiting Exoplanet Survey Satellite (TESS) light curves for 193,940 A-F stars selected from four legacy catalogs—the Henry Draper Catalogue (HD), the Smithsonian Astrophysical Observatory (SAO) Star Catalog, the Positions and Proper Motions Catalog (PPM), and the Bonner Durchmusterung (BD, including its extensions). Through visual inspection of light curve morphologies and periodograms, combined with evaluation of stellar parameters, we identified over 51,850 previously unreported variable stars. These include 15,380 $\delta$ Scuti, 18,560 $\gamma$ Doradus, 28 RR Lyrae stars, 260 heartbeat candidates, and 2645 eclipsing binaries, along with thousands of other variable types. Notably, over 4145 variables exhibit hybrid $\delta$ Scuti-$\gamma$ Doradus pulsations, and more than 380 eclipsing binaries feature pulsating primary components. This study reveals a substantial population of bright, previously undetected variables, providing a valuable resource for ensemble asteroseismology, binary evolution studies, and Galactic structure research. Our results also highlight the surprising richness in variability still hidden within well-known stellar catalogs and the continued importance of high-precision, time-domain surveys such as TESS. Full article

Apricot (Prunus armeniaca L.) exhibits a gametophytic self-incompatibility (GSI) system. To identify the S-genotypes of the main apricot cultivars, including 133 native Chinese cultivars and 35 foreign accessions, PCR was performed using a combination of five primers based on the conserved regions of Prunus S-RNase genes. After cloning and sequencing the PCR products, the S-genotypes of all 168 apricot cultivars were determined. A total of 46 different S-RNase alleles, with 15 new alleles, were identified. For all 168 accessions, the top five most frequent S-alleles were S₈, S₁₁, S₉, S₁₆, and S₅₃. S₁₁, S₈, and S₁₆ were the most frequent in Chinese cultivars, and S₉, S₈, and S₂ were mostly found in European accessions. For Chinese apricot cultivars, the distribution of S-alleles among five geographic regions was also investigated. In Northwest China, S₁₆ was the most frequent S-allele. In the Xinjiang region, S₆₆, S₄₉, and S₁₄ were the top three most frequent S-alleles. In North China, S₈, S₁₁, and S₅₃ were the top three most frequent S-alleles. In addition, the self-compatible type, S_C, was not detected in these 133 Chinese accessions. Finally, the phylogenetic tree of apricot S-alleles indicated that there are four groups of S-RNase genes (S₉₇/S₁₀₆, S₁₄/S_14a/S₆₆, S₉/S₁₇/S₄₄, and S₂₃/S₅₃) presenting a very close relation. These results provide more data on the S-genotypes of apricot accessions, which can support future breeding programs by aiding in the selection of the appropriate parents and contributing to efficient orchard design by combining cultivars with suitable pollinizers. Full article

Background/Objectives: Vertebroplasty (VP) and kyphoplasty (KP) are widely performed minimally invasive procedures for osteoporotic vertebral compression fractures and vertebral metastases. Although generally safe, postoperative surgical site infections (SSIs) can lead to severe complications. The true incidence of SSIs and optimal prophylactic antibiotic strategies remains unclear. This study evaluated SSI incidence and the impact of antibiotic timing and type using a nationwide quality assessment (QA) database in South Korea. Methods: We analyzed data from the 7th to 9th QA waves of the Health Insurance Review and Assessment (HIRA) Service, including 23,868 patients who underwent VP or KP. SSI incidence was compared across antibiotic timing groups (preoperative-only, postoperative-only, and combined) and antibiotic types. Multivariate logistic regression identified independent risk factors for SSIs. Results: SSI occurred in 47 patients (0.20% of 23,868 procedures). No infections were observed in the preoperative-only group, compared with 0.36% in the postoperative-only group and 0.19% in the pre- and postoperative group. The lowest incidence (0.16%) was seen with first- or second-generation cephalosporins. Multivariate analysis found no significant difference between the preoperative-only and the combined regimens, nor between first-/second-generation cephalosporins and broad-spectrum antibiotics. However, surgery at a tertiary hospital (aOR: 3.566) and malnutrition (aOR: 2.915) were independently associated with increased SSI risk. Conclusions: This nationwide study, the largest to date on VP and KP, demonstrated that SSIs are rare (0.2%). A single preoperative dose of first- or second-generation cephalosporins was as effective as combined or broader-spectrum regimens. Targeted preventive measures may be warranted for high-risk groups such as patients with malnutrition or those treated in tertiary hospitals. Full article

Excessive phosphorus emissions are a significant driver of severe eutrophication in water bodies, and developing an efficient and cost-effective adsorbent for phosphorus removal is imperative. In this study, a Na-type zeolite was synthesized from coal gangue sourced from an open-pit mine in Xinjiang province, China. The synthesis process involved drying, crushing, alkali activation, aging, hydrothermal crystallization, and Na⁺ ion exchange. Orthogonal design identified the optimal synthesis parameters: an alkali-to-ash ratio of 1:1, aging at 20 °C for 12 h, and crystallization at 130 °C for 12 h. Aging time exerted the greatest influence on the phosphate removal efficiency. The optimized zeolite exhibited excellent phosphate adsorption performance, achieving a removal efficiency of up to 96% and a capacity of 16 mg/g. The adsorption kinetics followed both pseudo-first-order and pseudo-second-order models, indicating processes governed by combined physical and chemical mechanisms. Isotherm data fitting with Freundlich and Langmuir models suggested the presence of both homogeneous and heterogeneous active sites. Thermodynamic studies confirmed a spontaneous and endothermic process, increasingly favorable at higher temperatures. Characterizations via scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy confirmed the formation of Na-type zeolite and revealed structural and compositional changes following phosphate adsorption. Aluminum and calcium binding played key roles in the chemical adsorption mechanisms. This work not only offers a high-efficiency, low-cost solution for phosphorus removal from wastewater but also provides a sustainable pathway for the valorization of coal gangue in the Zhundong area of Xinjiang, China. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Mineral identification technology is a key technology in the construction of intelligent mines. In ore classification and detection, mining scenarios present challenges, such as diverse ore types, significant scale variations, and complex surface textures. Traditional detection models often suffer from insufficient multi-scale feature representation and weak dynamic adaptability, leading to the missed detection of small targets and misclassification of similar minerals. To address these issues, this paper proposes an efficient multi-scale ore classification and detection model, YOLO-EDH. To begin, standard convolution is replaced with deformable convolution, which efficiently captures irregular defect patterns, significantly boosting the model’s robustness and generalization ability. The C3k2 module is then combined with a modified dynamic convolution module, which avoids unnecessary computational overhead while enhancing the flexibility and feature representation. Additionally, a content-guided attention fusion (HGAF) module is introduced before the detection phase, ensuring that the model assigns the correct importance to various feature maps, thereby highlighting the most relevant object details. Experimental results indicate that YOLO-EDH surpasses YOLOv11, improving the precision, recall, and mAP50 by 0.9%, 1.7%, and 1.6%, respectively. In conclusion, YOLO-EDH offers an efficient solution for ore detection in practical applications, with considerable potential for industries like intelligent mine resource sorting and safety production monitoring, showing notable commercial value. Full article