Search Results (1,780)

Age-related osteoporosis is driven in part by senescence-associated rewiring of bone marrow mesenchymal stem cells (MSCs) from osteogenic toward adipogenic fates. Accumulating evidence indicates that epigenetic drift and reduced autophagy are not isolated lesions but are mechanistically coupled through a bidirectional DNA methylation and autophagy axis. Here, we summarize how promoter hypermethylation of genes involved in autophagy and osteogenesis suppresses autophagic flux and osteoblast lineage transcriptional programs. Conversely, autophagy insufficiency reshapes the methylome by limiting methyl donor availability, most notably S-adenosylmethionine (SAM), and by reducing the turnover of key epigenetic regulators, including DNA methyltransferases (DNMTs), ten-eleven translocation (TET) dioxygenases, and histone deacetylases (HDACs). This self-reinforcing circuitry exacerbates mitochondrial dysfunction, oxidative stress, and inflammation driven by the senescence-associated secretory phenotype (SASP), thereby stabilizing adipogenic bias and progressively impairing marrow niche homeostasis and bone remodeling. We further discuss therapeutic strategies to restore balance within this axis, including selective modulation of epigenetic enzymes; activation of AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) signaling with downstream engagement of Unc-51-like autophagy-activating kinase 1 (ULK1) and transcription factor EB (TFEB); targeting sirtuin pathways; mitochondria- and autophagy-supportive natural compounds; and bone-targeted delivery approaches or rational combination regimens. Full article

Digital and green energy transitions are driving an unprecedented demand for Strategic and Critical Raw Materials (S-CRMs), necessitating the identification of alternative sources such as secondary raw materials from exploration and mining residues. This study investigates an integrated, multi-scale approach to map and recover S-CRMs from an abandoned exploration stockpile in Zlatá Baňa, Slovak Republic. A key aspect of the methodology is comprehensive chemical and mineralogical characterization (XRF, PXRD, FTIR, LIBS, and SEM-EDS), which provided scientific validation for the diagnostic absorption features observed in laboratory reflectance spectra. These laboratory-acquired signatures were then used as endmembers to classify Sentinel-2 imagery via the Spectral Angle Mapper (SAM) algorithm. This integration enabled the identification of three distinct residue classes, with classA (jarosite-rich residues) emerging as the most reactive facies. Subsequent bioleaching experiments using Acidithiobacillus ferrooxidans demonstrated that microbial activity more than doubled Zn mobilization compared to abiotic controls. This cross-disciplinary strategy confirms that the synergy between advanced analytical characterization and remote sensing provides a robust, cost-effective pathway for the sustainable recovery of S-CRMs in regions affected by historical and mining activities. Full article

Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM’s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount. Full article

While deep learning methods are increasingly applied in the field of DOA estimation, existing approaches generally feed the real and imaginary parts of the covariance matrix directly into neural networks without optimizing the input features, which prevents classical attention mechanisms from improving accuracy. This paper proposes a spatio-temporal fusion model named DACL-Net for DOA estimation. The spatial branch applies a two-dimensional Fourier transform (2D-FT) to the covariance matrix, causing angles to appear as peaks in the magnitude spectrum. This operation transforms the original covariance matrix into a dark image with bright spots, enabling the convolutional neural network (CNN) to focus on the bright-spot components via an attention module. Additionally, a spectrum attention mechanism (SAM) is introduced to enhance the extraction of temporal features in the time branch. The model learns simultaneously from two data branches and finally outputs DOA results through a linear layer. Simulation results demonstrate that DACL-Net outperforms existing algorithms in terms of accuracy, achieving an RMSE of 0.04${}^{\circ }$ at an SNR of 0 dB. Full article

Adlay (Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay’s unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop. Full article

This article examines the visual reception of the woman of Endor (1 Sam 28) and her gradual integration into the Western imaginary of the witch. In the first section, it offers a concise overview of the formation of witchcraft in late medieval and early modern visual culture, when iconographic and discursive registers contributed to the consolidation of a demonological and persecutory repertoire associated with the female body. Against this background, the study analyzes how the figure of Endor came to be interpreted and represented through increasingly negative categories—eventually becoming a conventionalized motif in the history of art—despite the fact that the biblical narrative originally presents her as a ritual mediator whose role in Saul’s episode is not constructed as a paradigmatic case of “witchcraft” in a strict sense. Drawing on a methodology of visual exegesis that brings together cultural biblical studies, art history, and gender studies, this article examines a range of artworks depicting the episode in order to show how visual culture negotiates the boundary between the legitimate and the forbidden, and how the later demonization of Endor reveals persistent tensions between orthodoxy and heterodoxy across different historical contexts. Full article

Post-collisional Cu-Au-Ni-Co-Pt-Pd-Sc porphyry [Duck Creek porphyry system (DCPS)] with overlying Au-Te-Bi-W-HRE epithermal mineralisation [Highway epithermal system (HES)] has been discovered in the core of the Mitakoodi anticline, southwest of Cloncurry. Xenotime and monazite geochronology indicate mineralisation occurred between ~1490 and 1530 Ma. Host rock lithologies show widespread potassic and/or propylitic to phyllic alteration. Paragenesis of porphyry sulphides indicates early crystallisation of pyrite, followed by chalcopyrite, with bornite forming by hydrothermal alteration of chalcopyrite. Cu sulphides also show the effect of supergene oxidation alteration with rims of covellite, digenite and chalcocite. Redox conditions deduced from the V/Sc systematics indicate that the DCPS contains both highly oxidised (typical of porphyries) and reduced lithologies, typical of plume-generated tholeiitic and alkaline suites. Ni/Te and Cu/Te systematics plot within the fields defined by epithermal and porphyry deposits. Duck Creek chalcophile and highly siderophile element (Cu, MgO and Pd) systematics resemble data from porphyry mineral systems, at Cadia, Bingham Canyon, Grasberg, Skouries, Kalmakyr, Elaisite, Assarel and Medet. SAM geophysical inversion models suggest the presence of an extensive porphyry system below the HES. A progressive increase in molar Cu/Au ratios with depth from the HES to the DCPS supports this conclusion. Three metal sources contributed to the linked DCPS-HES viz., tholeiitic ferrogabbro, potassic ultramafic to mafic system and an Fe and Ca-rich alkaline system. The latter two imparted non-crustal superchondritic Nb/Ta ratios that are characteristic of many deposits in the eastern Mount Isa Block. The associated tholeiite and alkaline magmatism reflect mantle plume upwelling through a palaeo-slab window that had accreted below the eastern flank of the North Australian craton following west-verging collision by the Numil Terrane. Discovery of this linked mineral system provides a new paradigm for mineral exploration in the region. Full article

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amphissa, Greece, with validation based on ground samples. We applied the well-established Linear Spectral Unmixing (LSU) and Spectral Angle Mapping (SAM) classification techniques utilizing endmembers of two established spectral libraries and incorporated ground data through geochemical and mineralogical analyses, X-ray fluorescence (XRF), Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), and X-ray Diffraction (XRD), to assess classification performance. The main lithologies in this area are bauxites and limestones; therefore, aluminum oxyhydroxides, calcite, and iron oxide minerals were the dominant phases as indicated by the XRF/XRD results. Almost all target minerals were mapped with the three sensors and both methods. The performance of EnMap is affected by its coarser spatial resolution despite its higher spectral resolution using these methods. Sentinel-2 is most effective for mapping iron-bearing minerals, particularly hematite, due to its higher spatial resolution and the presence of diagnostic iron oxide absorption features in the VNIR. World View 3 Shortwave Infrared (WV3-SWIR) performs better when mapping calcite, benefiting from its eight SWIR spectral bands and very high spatial resolution (3.7 m). Hematite and calcite yield the highest accuracy, especially with SAM, indicating 0.80 for Sentinel-2 (10 m) for hematite and 0.87 for WV3-SWIR (3.7 m) for calcite. AlOOH shows higher accuracy with SAM, ranging from 0.57 to 0.80 across the sensors, while LSU shows lower accuracy, ranging from 0.20 to 0.73 across the sensors. This study showcases each sensor’s ability to map minerals while also demonstrating that spectral coverage and the spatial and spectral resolution, as well as the characteristics of the selected endmembers, exert a critical influence on the accuracy of mineral mapping in mine waste. Full article

The spatial resolution of spectral imaging systems is fundamentally constrained by hardware trade-offs, and the availability of large-scale annotated spectral datasets remains limited. This study presents a comprehensive evaluation of super-resolution (SR) methods across interpolation-based, CNN-based, GAN-based, and diffusion-based approaches. Using a synthetic 30-band spectral representation reconstructed from RGB with the MST++ model as a proxy ground truth, we arrange non-adjacent triplets as three-channel PNG inputs to ensure compatibility with existing SR architectures. A unified pipeline enables reproducible evaluation at ×2, ×4, and ×8 scales on 50 unseen images, with performance assessed using PSNR, SSIM, and SAM. Results confirm that bicubic interpolation remains a spectrally reliable baseline; shallow CNNs (SRCNN, FSRCNN) generalize well without fine-tuning; and ESRGAN improves spatial detail at the expense of spectral accuracy. Diffusion models (SR3, ResShift, SinSR), evaluated in a zero-shot setting without spectral-domain adaptation, exhibit unstable performance and require spectrum-aware training to preserve spectral structure effectively. The findings underscore a persistent trade-off between perceptual sharpness and spectral fidelity, highlighting the importance of domain-aware objectives when applying generative SR models to spectral data. This work provides reproducible baselines and a flexible evaluation framework to support future research in spectral image restoration. Full article

To address the limitation of 2D methods in inferring absolute scrap dimensions from images, we propose Scrap-SAM-CLIP (SSC), a vision-language model integrating the segment anything model (SAM) and contrastive language-image pre-training in Chinese (CN-CLIP). The model enables identification of canonical scrap shapes, establishing a foundational framework for subsequent 3D reconstruction and dimensional extraction within the 3D recognition pipeline. Individual modules of SSC are fine-tuned on the self-constructed scrap dataset. For segmentation, the combined box-and-point prompt yields optimal performance among various prompting strategies. MobileSAM and SAM-HQ-Tiny serve as effective lightweight alternatives for edge deployment. Fine-tuning the SAM decoder significantly enhances robustness under noisy prompts, improving accuracy by at least 5.55% with a five-positive-points prompt and up to 15.00% with a five-positive-points-and-five-negative-points prompt. In classification, SSC achieves 95.3% accuracy, outperforming Swin Transformer V2_base by 2.9%, with t-SNE visualizations confirming superior feature learning capability. The performance advantages of SSC stem from its modular assembly strategy, enabling component-specific optimization through subtask decoupling and enhancing system interpretability. This work refines the scrap 3D identification pipeline and demonstrates the efficacy of adapted foundation models in industrial vision systems. Full article

Several studies have demonstrated that methionine supplementation in fish diets enhances immune status, inflammatory response, and resistance to bacterial infections by modulating for DNA methylation, aminopropylation, and transsulfuration pathways. However, the immunomodulatory effects of methionine in viral infections remain unexplored. This study aimed to evaluate the effect of methionine supplementation on immune modulation and resistance to the viral haemorrhagic septicaemia virus (VHSV) in rainbow trout (Oncorhynchus mykiss). Two diets were formulated and fed to juvenile rainbow trout for four weeks: a control diet (CTRL) with all nutritional requirements, including the amino acid profile required for the species, and a methionine-supplemented diet (MET), containing twice the normal requirement of DL-methionine. After feeding, fish were bath-infected with VHSV, while control fish were exposed to a virus-free bath. Samples were collected at 0 (after feeding trial), 24, 72, and 120 h post-infection for the haematological profile, humoral immune response, oxidative stress, viral load, RNAseq, and gene expression analysis. In both diets, results showed a peak in viral activity at 72 h, followed by a reduction in viral load at 120 h, indicating immune recovery. During the peak of infection, leukocytes, thrombocytes, and monocytes migrated to the infection site, while oxidative stress biomarkers (superoxide dismutase glutathione S-transferase, and glutathione redox ratio) suggested a compromised ability to manage cellular imbalance due to intense viral activity. At 120 h, immune recovery and homeostasis were observed due to an increase in the amount of nitric oxide, GSH/GSSG levels, leukocyte replacement, monocyte influx, and a reduction in the viral load. When focusing on the infection peak, gene ontology (GO) analysis showed several exclusively enriched pathways in the skin and gills of MET-fed fish, driven by the upregulation of several key genes. Genes involved in recognition/signalling, inflammatory response, and other genes with direct antiviral activity, such as TLR3, MYD88, TRAF2, NF-κB, STING, IRF3, -7, VIG1, caspases, cathepsins, and TNF, were observed. Notably, VIG1 (viperin), a key antiviral protein, was significantly upregulated in gills, confirming the modulatory role of methionine in inducing its transcription. Viperin, which harbours an S-adenosyl-L-methionine (SAM) radical domain, is directly related to methionine biosynthesis and plays a critical role in the innate immune response to VHSV infection in rainbow trout. In summary, this study suggests that dietary methionine supplementation can enhance a more robust fish immune response to viral infections, with viperin as a crucial mediator. The improved antiviral readiness observed in MET-fed fish underscores the potential of targeted nutritional adjustments to sustain fish health and welfare in aquaculture. Full article

This study presents a comprehensive techno-economic assessment to optimize a hybrid renewable energy system for green hydrogen production in Jordan. Using the Hybrid Optimization Model for Electric Renewables (HOMERs) and System Advisor Model (SAM) software, this study evaluates multiple cost projections for 2030 technology costs. Key parameters such as capital cost, efficiency, and lifetime are varied extensively. Highlighted results show a wide range in the Levelized Cost of Hydrogen (LCOH), reaching 1.59 to 3.49 USD/kg, and the Levelized Cost of Energy (LCOE) from 0.0072 to 0.0301 USD/kWh. Furthermore, Net Present Value (NPV) spans from USD 424 to 927 million, depending on the scenario and sensitivity case. Technically, the system’s optimized capacities vary significantly. PV ranges from 203 to 457 MW, wind capacities range from 0 to 220 MW, and electrolyzers range from 192 to 346 MW, demonstrating the flexibility required to meet different cost and performance assumptions. The study’s broad relevance extends to developing countries with grid constraints, where off-grid green hydrogen production is feasible. Its framework can be adapted globally, offering valuable insights. Full article

Diplodia shoot blight is an opportunistic fungal pathogen infesting many conifer species and it has a global distribution. Depending on the duration and severity of the disease, affected needles appear yellow (chlorotic) for a brief period before becoming red or brown in colour. These symptoms can occur on individual branches or over the entire crown. Aerial sketch-mapping or the manual interpretation of aerial photography for tree health surveys are labour-intensive and subjective. Recently, however, the application of deep learning (DL) techniques to detect and classify tree crowns in high-spatial-resolution imagery has gained significant attention. This study evaluated two complementary DL approaches for the detection and classification of Pinus radiata trees infected with diplodia shoot blight across five geographically dispersed sites with varying topographies over two acquisition years: (1) object detection using YOLOv12 combined with Segment Anything Model (SAM) and (2) pixel-level semantic segmentation using U-Net, SegFormer, and EVitNet. The three damage classes for the object detection approach were ‘yellow’, ‘red-brown’ (both whole-crown discolouration) and ‘dead tops’ (partially discoloured crowns), while for the semantic segmentation the three classes were yellow, red-brown, and background. The YOLOv12m model achieved an overall mAP50 score of 0.766 and mAP50–95 of 0.447 across all three classes, with red-brown crowns demonstrating the highest detection accuracy (mAP50: 0.918, F1 score: 0.851). For semantic segmentation models, SegFormer showed the strongest performance (IoU of 0.662 for red-brown and 0.542 for yellow) but at the cost of longest training time, while EVitNet offered the most cost-effective solution achieving comparable accuracy to SegFormer but with a superior training efficiency with its lighter architecture. The accurate identification and symptom classification of crown damage symptoms support the calibration and validation of satellite-based monitoring systems and assist in the prioritisation of ground-based diagnosis or management interventions. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

To address the challenge of effectively extracting early-stage failure features in rolling bearings, this paper proposes a two-stage harmonic optimization-gram method based on spectral amplitude modulation (SAM-TSHOgram). The method first employs amplitude spectra with varying weighting exponents to preprocess the signal, performing nonlinear adjustments to the vibration signal’s spectrum to enhance weak periodic impact characteristics. Subsequently, a two-stage evaluation strategy based on spectral coherence (SCoh) was designed to adaptively identify the optimal frequency band (OFB). The first stage employs the Periodic Harmonic Correlation Strength (PHCS) metric, based on autocorrelation, to coarsely screen candidate bands with strong periodic structures. The second stage utilizes the Sparse Harmonic Significance (SHS) metric, based on spectral negative entropy, to refine the candidate set, selecting bands with the most prominent harmonic features. Finally, SCoh is integrated over the selected OFB to generate an Improved Envelope Spectrum (IES). The proposed method was validated using both simulated and experimental vibration signals from bearings and gearboxes. The results demonstrate that SAM-TSHOgram significantly outperforms conventional approaches such as EES, Fast Kurtogram, and IESFOgram in terms of signal-to-noise ratio (SNR) enhancement, harmonic clarity, and diagnostic robustness. These findings confirm its potential for reliable early fault detection in rolling bearings. Full article