Search Results (9,194)

Alhagi camelorum is a dominant leguminous shrub distributed in the Taklamakan Desert, an area characterized by extreme drought and high soil salinization, which can complete its life cycle normally in salt-affected soils. However, the underlying molecular regulatory mechanism of its salt tolerance remains largely unclear. The AcABI5 gene was successfully cloned and characterized, and it encodes a typical nuclear-localized bZIP transcription factor. Functional characterization demonstrated that overexpression of AcABI5 markedly improved the salt stress tolerance of A. camelorum calli, whereas silencing of AcABI5 via virus-induced gene silencing (VIGS) rendered the plant more sensitive to salt stress. Further mechanistic investigations revealed that AcABI5 enhanced salt tolerance by regulating the expression of superoxide dismutase (SOD)- and peroxidase (POD)-related antioxidant genes. Compared with the wild type, AcABI5-overexpressing calli exhibited significantly increased SOD and POD activities and remarkably reduced malondialdehyde (MDA) content under salt treatment, whereas AcABI5-silenced lines exhibited the opposite physiological phenotypes. Furthermore, heterologous silencing of AcABI5 in Nicotiana benthamiana via virus-induced gene silencing (VIGS) produced comparable salt-sensitive phenotypes, similar to those observed in A. camelorum AcABI5-silenced lines. Collectively, these results provide insights into the molecular mechanism by which AcABI5 enhances salt tolerance in A. camelorum, and lay a solid theoretical foundation for the optimization of the A. camelorum genetic transformation system and the expansion of related salt-tolerant crop research. Full article

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study presents a Transformer model with geometric enhancement. The model combines local geometric features and global attention to improve species recognition in forest environments. It uses geometric information with biological meaning, including point cloud normals, local density, vertical structure, and growth direction. A focal loss with class balance is also introduced to reduce the impact of species distributions with long tails. Experiments on the ForSpecial20K dataset show that the proposed method performs better than representative models based on convolution, graph methods, and Transformer architectures. It achieves higher overall accuracy (78.20%), higher mean class accuracy (73.48%), and a higher Macro-F1 score (73.21%). Results from confusion matrices and visual analysis of similar species further verify the effectiveness of the geometric features and the loss design. These results suggest that modeling structural information of forests helps improve robustness and generalization. The proposed method offers a practical solution for tree-level species mapping, fusion of LiDAR data from multiple sources, and fine-scale forest inventory. It also shows the value of combining high-resolution LiDAR data with deep learning for forestry applications. Full article

Background/Objectives: Sosnowsky’s hogweed Heracleum sosnowskyi, which originated in the Greater Caucasus region and spread rapidly across Central and Eastern Europe after being introduced as cattle fodder in the 1950s, is an example of an extremely dangerous invasive species listed by the European Union. This study aimed to estimate the genetic diversity of 6 native populations of Sosnowsky’s hogweed from the Caucasus region of Russia and Georgia, as well as 15 invasive populations from Lithuania and Poland, and to assess the adaptability of hogweed in new environments. Methods: Genetic analyses of plant material were conducted, including DNA extraction, ISSR genotyping, PCR product separation, and subsequent molecular data mining and analysis. Results: A pairwise Mantel test revealed a positive correlation between geographical distance and the genetic diversity of the hogweed populations. The presence of three distinct allele pools was confirmed in the populations under study, with genotypes from Poland dominated by the first allele pool, which had the largest number of polymorphic and private loci. Analysis of molecular variance by origin showed that 99% of the variation was within the analysed hogweed populations, with only 1% being between them. Native populations from Russia were genetically distinct from those in Poland and Lithuania. Some of the Georgian population shows genetic similarities to Russians, while the rest shows similarities to the secondary invasive Lithuanians. Conclusions: Introduced populations of H. sosnowskyi are characterised by considerable genetic variation, likely resulting from multiple introductions and subsequent evolutionary processes, which may facilitate local adaptation and invasiveness, although overall large-scale genetic differentiation remains low. Full article

Accurate short-term photovoltaic (PV) power forecasting is essential for power system scheduling and market operations. Existing studies have shown the value of numerical weather prediction (NWP), graph-based spatial modeling, and temporal sequence learning, but the boundary of their contributions remains fragmented across many practical forecasting frameworks. In particular, adjacent multi-point NWP information is often not explicitly organized according to its spatial relationships, while historical similar-day power is rarely integrated with graph-structured meteorological features in a unified model. To address this gap, this study develops a short-term PV power forecasting framework that combines multi-point NWP graph construction with similar-day-guided Transformer fusion. First, predicted irradiance from the target site and neighboring NWP points is organized as a graph, and a Graph Convolutional Network (GCN) is used to extract local spatial meteorological features. Second, similar days are identified through a two-stage selection strategy based on Euclidean distance and Pearson correlation, and the corresponding historical power sequences are aggregated as temporal guidance. Finally, the graph-extracted NWP features, similar-day power, and predicted humidity are fused by a Transformer-based temporal modeling module to generate day-ahead PV power forecasts. Experimental results show that the proposed framework outperforms TCN-Transformer, Transformer, GCN, LSTM, and BP on the studied dataset, and maintains favorable performance on additional PV stations. These results indicate that the joint integration of graph-structured multi-point NWP information and historical similar-day power is effective for short-term PV power forecasting. Full article

In response to the subjective issues, inconsistent quality standards, high labor intensity and low sorting efficiency during the drying process of green pepper, an improved YOLOv11 algorithm was proposed for quality detection. A multi-scale edge enhancement module (MEEM) is introduced into the backbone network, replacing the original basic C3K2 module with C3K2-MEEM to enhance the extraction of detailed features in images of dried green Sichuan pepper and prevent missed detections, false detections, and boundary confusion. The LRSA module is integrated into the 10th layer of the backbone network to improve the clarity of the tumor-like texture of the Sichuan pepper and reduce the influence of impurities, automatically allocating attention based on feature similarity to preserve local information. In the neck layer, the DPCF module is added to the FPN+PAN feature fusion stage to achieve multi-scale feature collaboration, meeting the detection requirements of dried green Sichuan pepper. The results show that the accuracy recall rate, mean average precision, and model size of the improved MLD-YOLOv11 algorithm are 92.1%, 96.6%, 95.6%, and 11.06 MB, respectively. Compared with the training results of the original YOLOv11 model, the average accuracy of the improved model has increased by 2.2 percentage points, and GFLOPs have definitely decreased by 2 G, with parameter reduction of approximately 3.10%. Compared with other mainstream models, the MLD-YOLOv11 model has significant advantages in terms of mean average precision, model size, and floating point operations per second, making it more suitable for industrial applications and providing an efficient, accurate, and lightweight solution for the quality detection of dried green Sichuan pepper. Full article

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 $\mathsf{\mu }\mathrm{g}$ of iron, below the estimated 4.8 $\mathsf{\mu }\mathrm{g}$ sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

Arginase 1 (ARG1) deficiency (ARG1-D) is a rare genetic disorder due to loss of ARG1, the final enzyme in the urea cycle. ARG1-D hepatocytes are impaired in converting arginine into urea, resulting in elevated peripheral arginine and ammonia, which leads to progressive neurological symptoms. Current therapeutic strategies mainly focus on managing plasma arginine and ammonia level, but long-term outcomes remain poor. While no approved treatment specific for ARG1-D is available in the United States, a recombinant protein-based enzyme replacement therapy is available in Europe. Recently, extracellular vesicles (EVs) are emerging as a powerful therapeutic vehicle. By using Capricor’s StealthX^TM platform, EVs were engineered to express human ARG1 on their surface or encapsulated within. Regardless of their localization on the EV membrane, nanograms of ARG1 carried by EVs were biologically active and able to convert arginine into urea as potent as micrograms of human recombinant ARG1 (rHuArg1). Furthermore, ARG1-encapsulating EVs (STX-Arg1-in) were able to deliver ARG1 intracellularly but not EVs carrying ARG1 on their surface or rHuArg1. STX-Arg1-in EVs were further evaluated in a series of in vivo studies, and the results showed that STX-Arg1-in EVs were non-toxic and able to restore arginase activities in the liver of Arg1^−/− mice, which led to a lowered plasma arginine concentration similar to that in wild-type mice. Most importantly, Arg1-in EVs expanded the lifespan of the lethal neonatal Arg1 deficiency mouse model. Taken together, our data suggested StealthX^TM-engineered STX-Arg1-in EVs have a better safety profile due to the extremely low dosage and have great potential as a novel enzyme replacement strategy for patients suffering from ARG1-D. Significance statement: Intracellular delivery of recombinant protein and improved llifespanare endpoints of successful enzyme replacement therapy for the treatment of ARG1-D. Using the StealthX platform, a fully functional ARG1 enzyme was engineered to be carried inside of the extracellular vesicles, which allowed for the intracellular delivery of ARG1 protein in vitro and in vivo, with an improvement of lifespan in a lethal neonatal mouse model of Arg1 deficiency. More importantly, no toxicity was observed, and efficacy was achieved with a low dose, setting the base for an improved therapeutic approach. Full article

Protecting and preserving the agricultural heritage, folk culture and ecological environment of traditional villages is a key element in advancing the strategy for comprehensive rural revitalisation. This paper constructs a theoretical framework for tourism symbiosis, examines the level of tourism symbiosis in the 13 national-level traditional villages of Jia County, and proposes strategies for tourism development. This study employs the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, alongside spatial analysis techniques such as the Hotspot Analysis, to reveal the levels of tourism symbiosis in traditional villages and their spatial distribution. The results indicate that traditional villages are distributed along the Yellow River, with a linear clustering pattern particularly evident in the central region of Jia County; the overall level of symbiosis exhibits a spatial pattern of higher levels in the north and lower levels in the south, with uneven levels across various dimensions; The traditional villages are categorised into four symbiotic models: comprehensive advantage-led, cultural corridor-dependent, ecological and cultural tourism potential, and low-development conservation. Based on these categories, strategies are proposed to deepen the exploration of local culture, promote industrial integration and regional collaboration, prioritise ecological conservation and environmental restoration, and establish distinctive brands through the rational utilisation of surrounding resources. The research framework and conclusions of this paper provide methodological references and practical insights for the concentrated and contiguous protection of traditional villages, as well as for research on rural revitalisation and sustainable development. Full article

The development of realistic breast phantoms is critical for the evaluation of imaging systems and quantitative image analysis methods. In this work, breast samples derived from the same digital model were produced using 3D printing technology and evaluated for structural similarity and reproducibility. Four independently manufactured phantoms were imaged using mammography and breast tomosynthesis. Radiomic features were extracted from regions of interest in order to assess inter-phantom variability. The results showed very good agreement between the four printed phantoms. Most first-order and GLCM radiomic features exhibited very low inter-phantom variability, indicating consistent structural and intensity characteristics. Neighborhood-based texture features showed slightly higher variability, reflecting their sensitivity to local structural differences. Fractal and power spectrum analyses also confirmed the high structural similarity of the phantoms. These results indicate that the proposed manufacturing approach can produce reproducible breast imaging phantoms suitable for mammography and tomosynthesis imaging studies, with potential applications in imaging system evaluation and radiomic research. Full article

Energy transition includes the substitution of centralized energy systems with decentralized variable renewable energy sources (vRES), the growth of which brings drawbacks such as grid congestion and intermittency. These issues are increasingly troublesome in many local energy systems, including in The Netherlands. Biogas may provide options to provide backup renewable energy in times of energy supply uncertainty. In The Netherlands, the consideration of biogas in such functions is limited. Meanwhile, local energy initiatives (LEIs) are spearheading the adoption of vRES. Because of concern over local grid balancing, LEIs may want or need to innovate and diversify their activities. Such innovation could include bioenergy in general, and biogas specifically. However, only a small number of LEIs consider bioenergy, and Dutch LEIs seem hesitant to venture into biogas specifically. In this paper we explore the question of what hinders adoption of biogas in The Netherlands in general, and by LEIs specifically, deploying an approach based on the technological innovation systems (TIS) concept. In that approach, we take insights from current and expected policy in The Netherlands juxtaposed with insights from similar countries surrounding The Netherlands. We conclude that historic developments in biogas already created a moderately supportive platform for large-scale biogas development, but some essential factors remain inadequately developed. Key barriers to biogas innovation, especially for LEIs, are insufficient mobilization of financial and knowledge resources, and insufficient attention to alleviating preconceptions. Dependable support and attention for socio-economic factors in policymaking would improve conditions associated with resources, preconceptions and resistance, and the situation for LEIs to explore the potential of biogas. However, it remains uncertain whether such measures would be sufficient to improve the potential of local biogas utilization in The Netherlands in a way that opens a role for biogas in solving energy transition challenges such as energy system balancing. Full article

The precipitation characteristics and grain-boundary structure of Al–Cu–Mg alloys strongly affect their corrosion behavior, whereas the roles of Si and Ag microalloying in low Cu/Mg ratio systems are not yet fully understood. In this work, the effects of Si and Ag additions on age-hardening response, precipitation characteristics, and corrosion performance were systematically investigated by combining transmission electron microscopy with electrochemical and corrosion measurements. Si addition significantly accelerated the age-hardening kinetics, enabling the alloy to reach a hardness of 147 HV after only 6 h of aging, whereas the base alloy required 24 h to reach a similar level. This accelerated response was accompanied by refined S-phase precipitation and a markedly narrowed precipitation-free zone along grain boundaries. Further Ag addition introduced coherent Ω precipitates and a more complex multi-phase precipitation structure, which increased microstructural heterogeneity. As a result, the Al–Cu–Mg–Si alloy exhibited the lowest corrosion current density and the shallowest corrosion depth, whereas the Al–Cu–Mg–Si–Ag alloy showed deteriorated corrosion resistance. These results indicate that Si microalloying alone can simultaneously accelerate aging and improve corrosion resistance, while further Ag addition enhances precipitation complexity and strengthening potential but increases susceptibility to localized corrosion. Full article

Hydrogen-enriched fuel injection in staged gas-turbine combustors is commonly achieved through jet-in-crossflow (JICF) configurations, where flame stabilization is governed by a local balance between flow-induced strain/mixing and chemical reaction rates. This work investigates turbulent reacting JICF relevant to staged combustion conditions using high-fidelity simulations with adaptive mesh refinement (AMR) and differential-diffusion effects together with Lagrangian particle statistics. Chemistry model uncertainties are incorporated by using a projection method that maps uncertainty estimates from detailed mechanisms into the model used in this work. Results show that the macroscopic flame topology remains in a stable two-branch regime (lee-stabilized and lifted) and is primarily controlled by the jet momentum–flux ratio J. Visualization of the normalized scalar dissipation rate reveals that the flame front resides on the low-dissipation side of intense mixing layers, occupying an intermediate region between over-strained and under-mixed regions. While hydrogen content does not significantly change the global stabilization mode for the cases studied, uncertainty analysis reveals composition-dependent differences that are not apparent in the mean behavior alone. In particular, visualization in Eulerian (χ, T) state-space analysis and particle statistics conditioned on the stoichiometric surface demonstrate that higher-hydrogen cases observe a lower scalar dissipation rate and exhibit substantially reduced variability in OH production under kinetic-parameter perturbations, whereas lower-hydrogen blends experience higher dissipation and amplified chemical sensitivity. These findings highlight that, even in globally similar JICF regimes, the hydrogen content can modify the local response of the flame to kinetic-parameter uncertainty, motivating uncertainty-aware interpretation and design for hydrogen-fueled staging systems. Full article

Semantic segmentation of land use and land cover (LULC) in arid regions remains challenging due to severe class imbalance, fragmented spatial distributions, and high spectral similarity among different land cover types. These characteristics often lead to an information bottleneck in deep segmentation networks and hinder the extraction of discriminative semantic representations. To address these issues, we propose SDS-Former, a lightweight semantic segmentation network specifically designed for remote sensing imagery in arid environments. SDS-Former incorporates an SSM-inspired Lightweight Semantic Enhancement (LSE) module to strengthen contextual modeling and alleviate the loss of discriminative information in deep features. To tackle scale variations, a Dynamic Selective Feature Fusion (DSFF) module is employed in the decoder to adaptively weight and fuse high-level semantics with low-level spatial details. Furthermore, a Feature Refinement Head (FRH) is introduced to enhance boundary localization and improve the recognition of small-scale and sparsely distributed land cover objects. Extensive ablation and comparative experiments demonstrate that SDS-Former consistently outperforms representative semantic segmentation methods across multiple evaluation metrics. On the Tarim Basin dataset, the proposed network achieves a mean Intersection over Union (mIoU) of 82.51% and an F1 score of 86.47%, indicating its superior effectiveness and robustness. Qualitative results further verify that SDS-Former exhibits clear advantages in distinguishing spectrally similar land cover types and preserving the spatial continuity of ground objects in complex arid-region scenes. Full article

The simultaneous analysis of geometric morphology and thermodynamic states from heterogeneous sensing modalities is essential for high-temperature industrial inspection. While supervoxel segmentation is effective for extracting fine structures, conventional fixed-weighting schemes often struggle with the inherent heterogeneity between spatial sensors and thermal sensors. This paper proposes a segmentation framework for thermo-geometric point clouds based on Pareto-optimal weight learning and gradient anisotropy. A multi-objective evolutionary optimization algorithm is employed for multi-modal Pareto weight learning to adaptively balance geometric and thermal constraints. The developed gradient-anisotropic supervoxel generation algorithm introduces a local saliency factor to achieve fine-grained thermodynamic segmentation. Furthermore, a gradient damping mechanism is implemented to ensure high thermal-boundary adherence even in geometrically planar regions by imposing anisotropic penalty forces. Finally, a region-growing method based on the optimized multi-sensor fusion weights is utilized to merge similar supervoxels. Experimental results demonstrate that our approach outperforms traditional baselines by achieving high-fidelity thermal segmentation and multi-modal boundary preservation, while accepting a modest and necessary compromise in geometric compactness to accommodate spatial–thermal inconsistencies. Full article

Background/Objectives: Temporal predictability may sharpen our ability to distinguish similar sounds, but whether this relies on attention is unclear. This study examined how temporal structure influences frequency discrimination. Methods: Thirty-six adults completed active (attend) and passive (ignore) listening tasks across three paradigms that varied in temporal structure: oddball (isolated deviants), two-tone frequency discrimination paradigm (pairs comparison), and local irregularity of the local/global paradigm (five-tone sequences, bundles). Stimuli varied in difficulty via small or large frequency deviations. Behavioral responses and subjective ratings were collected during active and passive listening. EEG was recorded to assess mismatch negativity (MMN) (either early MMN (eMMN) or mismatch response (MMR)) and P300 event-related potentials. Results: Under active listening, temporal predictability significantly improved performance, but only for difficult discriminations. The local-irregularity condition yielded higher hit rates and greater perceptual sensitivity (d’) than the other paradigms. This benefit was accompanied by enhanced P300, yet participants rated the conditions as equally difficult, indicating no metacognitive awareness. Under passive listening, predictability helped only for easy stimuli, marked by a larger MMR. No reliable change-detection response occurred for difficult sounds when attention was diverted. Conclusions: These findings suggest that the combination of temporal predictability and repeated standard presentation in the local irregularity paradigm can improve frequency discrimination under challenging, attended conditions, with some evidence for partial dissociation between objective performance and subjective awareness. However, substantial individual variability and cross-paradigm confounds caution against strong causal claims. These results are broadly consistent with predictive coding frameworks but require replication with counterbalanced designs and larger deviant trial counts. Full article