Search Results (3,279)

This study examines the occurrence of bacteria resistant to antibiotics and heavy metals in Terra Nova Bay, a coastal area of the Ross Sea in Antarctica that is increasingly recognised as vulnerable to human influence. During the 37th Italian Antarctic Expedition (2021–2022), researchers collected seawater, sediment, and fish samples from the notothenioid species Trematomus bernacchii to evaluate microbial resistance in an environment once considered largely pristine. Fifty heterotrophic bacterial isolates were obtained and tested against twenty-eight antibiotics, revealing a notable presence of multidrug resistance. These multidrug-resistant isolates were then assessed for their tolerance to eight heavy metal salts to understand whether resistance traits extended beyond antimicrobials. Twelve isolates showing resistance to both antibiotics and metals were selected for further genetic screening, targeting key resistance genes linked to tetracycline, vancomycin, sulphonamides, and other antimicrobial classes. The detection of multiple resistance genes in genera such as Pseudomonas, Pseudoalteromonas, and Psychrobacter indicates that both natural selective pressures and local, human-related contamination may be shaping resistance patterns in this region. Overall, the study demonstrates that even remote Antarctic marine ecosystems can host bacteria with complex resistance profiles. While these ecosystems are largely isolated, human activities such as scientific research, tourism, and the introduction of pollutants may contribute to the dissemination of antibiotic resistance genes, raising important ecological and potential public health considerations regarding the spread of resistance in polar environments. Full article

Background/Objectives: The purpose of this study was to identify shared and differing characteristics of individuals testing for both SARS-CoV-2 and MPXV in 2022 in the greater Houston metro area. Methods: Data from the Houston Electronic Disease Surveillance System (HEDSS) identified 7,754,198 SARS-CoV-2 PCR lab results and 1246 MPXVV PCR lab results in 2022. Three cohorts for analysis were created where tests were performed, as follows: those positive for both viruses, those negative for COVID-19 but positive for MPXV, and those positive for COVID-19 but negative for MPXV. Results: We identified 88 individuals positive for both viral infections, those negative for COVID-19 but positive for MPXV (n = 38), and those positive for COVID-19 but negative for MPXV (n = 96). While groups were generally similar in regard to demographics (age, sex, and race) and risk factors reported, key differences in timing of testing and risk factors were reported. Notably, there was statistically significant difference in the time between t-tests for dual-infected individuals (99 days) compared to MPXV-positive only (58 days, p < 0.01) or COVID-19 positive only (63 days, p < 0.01). Conclusions: In the setting of multiple disease outbreaks, the characteristics of infected patients may be largely similar. Some people with dual infection may show unusual test results or symptom patterns compared with those with only one infection. Large public health studies with robust reporting systems and laboratory screening are vital for early detection of dual infections. Public health strategies to educate providers and outreach teams enhance response during concurrent outbreaks. Further research is needed on behavior and risk factors in communities with simultaneous outbreaks. Full article

Load forecasting plays a vital role in key areas such as energy forecasting and resource management. Traditional forecasting methods are often limited in dealing with shifts in statistical distribution and dynamic changes in periodic parameters of load data, making it difficult to capture complex temporal dependencies and periodic change patterns. To address this challenge, we transform load data into the frequency domain and use a fine-tuned large language model for forecasting. Specifically, we propose the Frequency-Based Spatial–Temporal Mixture Learning Model (FSTML), which uses (1) a Frequency-domain Global Learning Module (FGLM), (2) Temporal-Dimension Learning Module (TDLM), and (3) Spatial-Dimension Learning Module (SDLM) to process load data and extract comprehensive temporal patterns. FGLM transfers load data to the frequency domain and provides the model with a frequency-domain global feature representation of load data. The TDLM and SDLM fine tune the pre-trained large language model in the time dimension and space dimension, respectively, extracting the temporal dependency and spatial dependency of load data, respectively, thereby extracting the spatial–temporal pattern of load data. FSTML achieves the best performance in the forecasting task on two public load datasets, and the forecasting accuracy is significantly improved. The high-precision load forecasting model proposed in this study can significantly improve the operational efficiency of power systems and the integration capacity of renewable energy sources, thereby supporting the sustainable development of the power industry in three dimensions: energy optimization, emission reduction, and economic operation. Full article

Endoscopic ultrasound (EUS) with fine-needle biopsy (FNB) is one of the techniques applied for sampling subepithelial lesions (SELs) of the gastrointestinal tract. Elastography and contrast-enhanced evaluation could permit identification of different patterns among areas of the lesions, depending on their consistence and the presence of vital cells or necrosis. Targeting a specific area when performing FNB in the case of large lesions could potentially permit an increase in accuracy and reduce the need for re-sampling. A 61-year-old woman was admitted reporting severe abdominal pain. The patient underwent cholecystectomy many years ago. She had no known family history of gastrointestinal, hepatic, biliary, or pancreatic disease. Laboratory tests were normal. A computed tomography scan showed a large lesion between the stomach and the pancreatic body, suspected to originate from the gastric wall. An endoscopic view showed a large bulging into the gastric lumen and EUS identified a lesion originating from the muscular layer of the gastric wall. Elastography and contrast-enhanced EUS identified two different areas, one softer with lower enhancement (A) and the other harder with higher enhancement after contrast injection (B). FNB was performed targeting both the areas, sending samples for separate histological evaluation. Histology showed a gastrointestinal stromal tumor (GIST), finding differences in amount of necrotic and neoplastic cells between the two areas. EUS-FNB guided by elastography and/or contrast-enhanced EUS could identify differences within large SELs, allowing targeting of areas more likely to collect diagnostic samples. Full article

Electrospinning, Forcespinning^®, and melt electrowriting are becoming increasingly important fiber-based fabrication technologies for tissue engineering and drug delivery applications. Despite their scientific and industrial relevance, their patent landscape has not been systematically examined, which limits the understanding of technological dynamics and translational applications. This study addresses this gap through a patentometric analysis conducted within a Competitive Technology Intelligence framework. A total of 3557 active and granted Extended Patent Families from 2020 to 2024 were analyzed to identify temporal patterns, geographic distribution, key innovators, industrial sectors, and primary application areas. The results showed that the overall patent activity increased until 2022 before experiencing a slight decline. China dominates the landscape, accounting for approximately 62% of applications filed, largely driven by academic institutions such as Shanghai University. Leading industries include special-purpose machinery, medical and dental technology, and textiles. According to International Patent Classification codes, filament formation (D01D5/00) is prevalent, while electrospinning—specifically IPC D04H1/728—represents the most active and influential of the three technologies. These findings exhibit the technological dynamics shaping fiber-based fabrication platforms and underscore their growing relevance in pharmaceutical innovation. The identified trends position these technologies as foundational for next-generation biomaterial design, offering valuable insights for researchers, industry stakeholders, and policymakers. Full article

(1) Background: Geothermal resources are enriched in Jiangsu Province, particularly in its mid-deep geothermal reservoirs. The thermal properties and thermal effects of magmatic rocks, which are largely unknown in Jiangsu Province, are fundamental for analyzing the genetic mechanisms of geothermal resources and evaluating resource potential. (2) Methods: Representative magmatic rock samples from different geological periods and different tectonic settings are collected from the main tectonic units of Jiangsu Province. Key thermophysical parameters such as thermal conductivity, heat production rate, rock density, and porosity are systematically tested. (3) Results: The variation patterns of these thermal property parameters are analyzed, and the sources and spatiotemporal evolution characteristics of radiogenic heat production, and the thermal effects of magmatic rocks, are specifically explored. (4) Conclusions: Magmatic rock lithology from acidic to basic is negatively correlated with thermal conductivity, thermal diffusivity, and radiogenic heat production rate, and positively correlated with volumetric heat capacity. The radiogenic heat production of magmatic rocks is primarily controlled by the contents of U and Th, increasing with the increasing SiO₂ content. The formation of geothermal anomalies in areas with thin or absent sedimentary cover is significantly influenced by the thermal effect of magmatic rocks, especially by the high heat-producing granites. The radioactive thermal contribution of the Taolin and Suzhou plutons was calculated. Full article

This study focuses on a large-span highway tunnel in argillaceous soft rock. Numerical simulations were conducted to investigate the mechanical characteristics of the tunnel, constructed using the Double Side Drift Method (DSDM), and the effects of the offset distance between drift faces. Subsequently, field monitoring was performed to analyze the deformation patterns of the primary support at typical cross-sections. The results indicate the following: (1) During DSDM construction in argillaceous soft rock, the crown settlement of the left drift is the largest, while that of the central drift is the smallest. The left and right drifts converge inward, whereas the central drift expands outward, resulting in overall inward convergence of the tunnel section, with the left drift exhibiting a larger convergence. The crown settlement and horizontal convergence induced by excavation of the upper benches of each drift are greater than those caused by the lower benches. (2) The stresses in the primary support increase rapidly after excavation of each segment and then tend to stabilize. The maximum tensile stress occurs at the left haunch, reaching 0.41 MPa, while the maximum compressive stress occurs at the left arch waist, reaching 14.56 MPa. After the tunnel excavation is completed and the section is enclosed, the stress on the left side is significantly higher than that on the right, indicating an eccentric stress state. The plastic zones in the surrounding rock exhibit a butterfly-shaped distribution, mainly concentrated at the haunches and arch springings on both sides. (3) As the offset distance decreases, the deformation of the primary support reduces, whereas the stress and the area of the surrounding rock plastic zones increase. When the offset distance is less than 15 m, both the stress in the primary support and the plastic zone area increase sharply, suggesting that the drift face offset distance should not be less than 15 m. (4) Field monitoring shows that the maximum cumulative crown settlement of the primary support reaches 30.2 mm, and the cumulative horizontal convergence of the section is 35.6 mm, both of which are below the reserved deformation allowance. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

Background: Inflammatory bowel disease (IBD) is a global disease with a considerable increase in prevalence and the impact on the health and well-being of patients suffering from this condition is vast. Diet has been suspected of being a contributor to IBD severity as well as intake of antibiotics. Methods: A literary search was conducted on the most recent studies on the subject of IBD, diet, and medical treatment to identify high-quality research findings within this area of research. Research published within the last decade was prioritized. Studies in English language were included in the search, and the knowledge gained was synthesized in the review. Results: Dietary patterns, specifically intake of Westernized diets, were associated with increased inflammation and increased disease severity in patients suffering from IBD, specifically patients suffering from Crohn’s disease (CD). A co-administration of pre- and probiotics was found to contribute to disease remission in ulcerative colitis patients, however, to a lesser extent in patients with CD. A bidirectional effect on the intestinal microbiome was seen as a result of intake of the medicines used for the treatment of IBD patients, which affects both bioavailability of the drug and efficacy of the treatment. The baseline composition of the intestinal microbiome in IBD patients dictates their response to the different treatments. Conclusions: Diet and medical treatment both have a large impact on the architecture of the intestinal Microbiome in IBD patients and are, as such, both essential to understand to enable individualized and optimized treatment. Full article

Organic field-effect transistors (OFETs) require reliable micro- and nanoscale patterning of semiconducting layers, yet conjugated polymers have long been considered incompatible with photolithography due to dissolution and chemical damage from photoresist solvents. Here, we present a photolithography-compatible strategy based on doping-induced solubility conversion (DISC), demonstrated using poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTT). AuCl₃ doping reversibly modulates the benzoid/quinoid resonance balance, lamellar stacking, and π–π interactions, suppressing solubility during lithographic exposure, while dedoping restores the intrinsic electronic properties. Using this approach, micropatterns with linewidths as small as 2 µm were fabricated in diverse geometries—including line arrays, concentric rings, dot arrays, and curved channels—with high fidelity; quantitative analysis of dot arrays yielded mean absolute errors of 48–66 nm and coefficients of variation of 2.0–3.9%, confirming resolution and reproducibility across large areas. Importantly, OFETs based on patterned PBTTT exhibited charge-carrier mobility, threshold voltage, and on/off ratios comparable to spin-coated devices, despite undergoing multiple photolithography steps, indicating preservation of transport characteristics. Furthermore, the same DISC-assisted lithography was successfully applied to other representative p-type conjugated polymers, including P3HT and PDPP-4T, confirming the universality of the method. This scalable strategy thus combines the precision of established lithography with the functional advantages of organic semiconductors, providing a robust platform for high-density organic electronic integration in flexible circuits, biointerfaces, and active-matrix systems. Full article

The 2025 large-scale forest fire in North Gyeongsang Province (Gyeongbuk) caused habitat fragmentation and disrupted ecological networks. This study quantitatively assessed both structural and functional connectivity loss and derived scientifically grounded restoration priorities. Fire intensity was assessed using Sentinel-2-based dNBR, and connectivity changes before and after the fire were analyzed by integrating MSPA (Morphological Spatial Pattern Analysis) and Omniscape (circuit theory-based model). MSPA captured extreme fragmentation, showing an 84% reduction in core habitats and a 976% increase in isolated patches, but failed to reflect functional movement flows. Omniscape approximated this using circuit theory, quantifying a 60% loss in cumulative current flow within the fire boundary and confirming that structural disconnection led to functional connectivity collapse. The restoration priority assessment (53 patches), based on source–sink theory, identified 14 high-priority patches (66% of total area). These patches were characterized by their adjacency to undamaged external cores, which serve as potential population sources for post-restoration recolonization. Notably, the top-priority areas were identified as key connection points within the national ecological corridor where Juwangsan National Park, the Nakdong Ridge, and Grade 1 Ecological Natural Areas overlap. This study demonstrated that integrating MSPA with Omniscape can simultaneously quantify both morphological fragmentation and functional disconnection caused by forest fires. This framework suggests that restoration planning should consider connectivity with broader ecological networks, in addition to recovering lost habitat area. Full article

Additive manufacturing has huge development potential in the aerospace field. The hot-end components of aeroengines work in harsh environments, facing high temperatures and a demand for long service life. In this paper, high-cycle fatigue (HCF) tests of Ti6Al4V alloy at 400 °C by selective laser melting (SLM) under different stress ratios (−1, 0.1, 0.3, 0.5, and 0.8) were carried out, and the fracture surfaces were observed. The results show that the defects existing on the surface or subsurface are prone to become the origin of fatigue cracks. There is a large dispersion of the high-cycle fatigue life of the samples, especially at a low stress ratio. With the increase in the stress ratio, the fatigue failure area on the fracture surface gradually decreases, and the fracture surface gradually presents a mixed pattern of tensile endurance fracture and fatigue failure. Considering the influence of creep damage due to mean stress, models were established, respectively, for the fatigue behavior and time-related rupture behavior to predict fatigue life and conduct an assessment. Then, the two models were combined and the composite models were proposed using the linear damage law. Finally, the single fatigue model and rupture models, as well as the composite models, were evaluated, respectively, and compared with the actual fatigue life, and the best model was obtained for the high-cycle fatigue prediction of SLM Ti6Al4V at 400 °C. Full article

Anthracnose is one of the major diseases affecting Dalbergia odorifera T. Chen. However, the soil microbial mechanisms underlying D. odorifera responses to anthracnose remain largely unexplored. This study investigated three planting systems: a Dalbergia odorifera monoculture (J); a mixed plantation of D. odorifera and Pterocarpus macrocarpus (JD); and a composite mixed plantation of D. odorifera, P. macrocarpus, and Clinacanthus nutans (JDY). Using amplicon sequencing technology for soil microbial analysis and combining soil physical and chemical properties with disease severity, we comprehensively analyzed changes in soil microbial community structure and function across different planting modes. The results showed that the diverse mixed mode (JD, JDY) significantly improved soil physicochemical properties and promoted soil nutrient cycling. Redundancy analysis (RDA) indicated that soil organic matter (SOM) and disease severity, quantified by the area under the disease progress curve (AUDPC), were the primary environmental drivers of microbial community variation. Genera positively correlated with SOM and negatively correlated with AUDPC were significantly enriched in JDY and JD, whereas genera showing opposite relationships were predominantly enriched in J. Functional predictions revealed enhanced nutrient-cycling capacities in JD and JDY, with JDY uniquely harboring functional groups such as Arbuscular Mycorrhizal, Epiphyte, and Lichenized taxa. In contrast, microbial functions in the J plantation were mainly limited to environmental amelioration. Co-occurrence network analysis further showed that as planting patterns shifted from J to JDY, microbial communities evolved from competition-dominated networks to cooperative defensive networks, integrating efficient decomposition with strong pathogen suppression potential. The study demonstrates that complex mixed planting systems regulate soil properties, enhance the enrichment of key functional microbial taxa, reshape community structure and function, and ultimately enable ecological control of anthracnose disease. This study provides new perspectives and theoretical foundations for ecological disease management in plantations of rare tree species and for microbiome-based ecological immunization strategies. Full article

Productive landscapes are an important part of intangible cultural heritage, and their protection and inheritance are of great significance to the prosperity and sustainable development of national culture. It not only reflects the wisdom accumulated through the long-term interaction between human production activities and the natural environment, but also carries a strong symbolic meaning of rural culture. However, current research and investigation on productive landscapes still rely mainly on field surveys and manual records conducted by experts and scholars. This process is time-consuming and costly, and it is difficult to achieve efficient and systematic analysis and comparison, especially when dealing with large-scale and diverse types of landscapes. To address this problem, this study takes the Inner Mongolia region as the main research area and builds a productive landscape feature data framework that reflects the diversity of rural production activities and cultural landscapes. The framework covers four major types of landscapes: agriculture, animal husbandry, fishery and hunting, and sideline production and processing. Based on artificial intelligence and deep learning technologies, this study conducts comparative experiments on several convolutional neural network models to evaluate their classification performance and adaptability in complex rural environments. The results show that the improved CEM-ResNet50 model performs better than the other models in terms of accuracy, stability, and feature recognition ability, demonstrating stronger generalization and robustness. Through a semantic clustering approach in image classification, the model’s recognition process is visually interpreted, revealing the clustering patterns and possible sources of confusion among different landscape elements in the semantic space. This study reduces the time and economic cost of traditional field investigations and achieves efficient and intelligent recognition of rural productive landscapes. It also provides a new technical approach for the digital protection and cultural heritage transmission of productive landscapes, offering valuable references for future research in related fields. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article