Search Results (74,417)

Background/Objectives: Sea urchin gonads (“roe”) are a valuable seafood product and a chemically complex matrix whose composition varies with physiology and environment. We present a biphasic extraction and ¹H NMR workflow to build a reusable reference inventory of polar metabolites and apolar lipid features in Paracentrotus lividus. Methods: Gonads from 37 adults (23 males, 14 females) collected at two sites (Alicante and Jávea–Dénia, Spain; October 2024) were lyophilized, extracted with methanol/chloroform/water, and analyzed by 400 MHz ¹H NMR in buffered aqueous solution (polar) and CDCl₃ (apolar). Polar metabolite identification combined 1D patterns with database matching and ¹H–¹³C HSQC confirmation on representative samples, yielding 71 annotated resonances corresponding to 37 metabolites spanning amino acids, osmolytes/quaternary amines, carbohydrates/aminosugars, and nucleoside/purine-related compounds. Results: Polar fingerprints enabled supervised modelling: PLS-LDA separated sexes with low cross-validated error, and SPA/COSS ranking highlighted glycine, alanine, creatine and osmolyte-associated signals as key discriminants; pathway mapping supported the enrichment of amino-acid and one-carbon/purine networks. Apolar spectra were annotated at the motif level and used for lipid-index estimation, indicating substantial unsaturation but low docosahexaenoic acid (DHA) and modest sex effects. Conclusions: The curated peak lists and reporting framework facilitate reproducible NMR annotation and future comparative studies of P. lividus gonads. Full article

This study investigated sulphide textures and trace element chemistry from the Tomingley Gold Project (TGP) region of Central-West NSW, eastern Australia, using in situ techniques. In particular, the study focused on pyrite and arsenopyrite to gain insights into ore-forming processes and determine which trace elements within these minerals can be used as potential pathfinder elements for mineral exploration in the TGP. A total of 41 drill core samples from a variety of lithologies (volcaniclastic, monzodiorite, graphitic siltstone, dacite, andesite) were described and analysed using reflected light microscopy, high-resolution microscopy (via Scanning Electron Microscope or SEM), elemental mapping (via Electron Probe Micro Analysis or EPMA) and targeted trace element analysis of sulphide grains (via Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry or LA-ICP-MS). Findings show that pyrite and arsenopyrite are the major sulphides that host fracture-fill/inclusions of native gold and ‘invisible gold’. Pyrite rich in groundmass inclusions should be evaluated due to their characteristic high concentrations of both As and Au. Pyrite trace element chemistry (Sn, Bi, W, Sb, Au and Se) was able to delineate mineralised from unmineralised samples in volcaniclastics, graphitic siltstones and andesites but was much more challenging for lithologies like dacites and monzodiorites. The study also found that Au may have been introduced into the system earlier and existed as ‘invisible gold’ in earlier generations of pyrite. This study highlighted the utility of in situ techniques to discriminate mineralised signatures from unmineralised samples, and this has proven to be far more effective compared to whole-rock techniques, emphasising the benefits of such datasets in mineral exploration. Full article

In the study of eggplant (Solanum melongena L.), a cross between the green peel line 19143 and the white peel line 19147 produced E4957 F₁ hybrids with a purple–brown peel. Self-fertilization of the F₁ hybrids yielded E4957 F₂ offspring with a segregation ratio of 27:9:21:7 among individuals with purple–brown, purple–red, green, and white peel colors, respectively, which was consistent with a genetic model controlled by reciprocal recessive epistasis between D and P, and Gv1 likely acting as a modifying factor. The green peel line 19143 exhibited higher chlorophyll but lower anthocyanin levels than the white peel line 19147, which contained low levels of both pigments, while the E4957 F₁ hybrids had elevated levels of both pigments. Two epistatic genes, D and P, associated with anthocyanin synthesis, were mapped on chromosomes 10 and 8, respectively. The putative modifying locus Gf, involved in chlorophyll accumulation in the flesh, was mapped on chromosome 8, and the localization interval was close to the previously reported Gv1 locus associated with chlorophyll synthesis in the peel. DNA markers (InDel22522, InDel5531, InDel-APRR2) were developed to genotype 237 F₂ individuals and correlate genotypes with phenotypes. Sequence analysis revealed a 6 bp deletion in the SmMYB1 (D) gene and a large deletion in the SmAPRR2-Like (Gv1) gene in the white peel line 19147, as well as a T to A mutation in the SmANS (P) gene in the green line 19143. This study provided evidence for inheritance between loci involved in anthocyanin and chlorophyll pathways contributing to eggplant peel color variation and provides molecular markers that may facilitate the breeding of eggplant varieties with diverse peel colors. Full article

The inspection of minuscule insulator defects from high-resolution (HR) UAV imagery presents a significant algorithmic challenge. The severe scale mismatch between HR images and low-resolution model inputs often leads to feature distortion for sparsely distributed targets. To address these issues, this paper proposes an integrated data–model collaborative framework. At the data level, an offline label-guided optimal tiling (LGOT) strategy is introduced to alleviate scale mismatch by curating information-dense training tiles. At the model level, we design the semi-decoupled prior-driven detection head (SDPD-Head), which leverages evolutionary priors to stabilize the learning of microscopic spatial features. During inference, an online inference-time adaptive tiling (ITAT) strategy is used to match the spatial scale distribution between training and inference and to reduce feature loss caused by direct downscaling. Experiments on a real-world inspection dataset show that the proposed framework achieves an mAP@50 of 92.9% with 2.17 M parameters and 4.7 GFLOPs. Full article

To address the topological mismatch between signal space and physical space caused by uneven signal feature distribution in indoor non-line-of-sight and complex topological environments, this paper proposes an indoor positioning algorithm based on Energy-domain Fingerprint Manifold Graph Total Variation (EFM-GTV). To mitigate neighborhood distortion caused by uneven high-dimensional signal feature distribution, a UMAP manifold topology graph construction method based on fuzzy simplicial sets is designed to establish a graph basis consistent with physical space topology. To reduce false matching risks in global search, a physical topology pruning strategy combining Jaccard similarity is proposed, effectively eliminating pseudo-connections. Building upon this foundation, we introduced an optimization model based on graph total variation, reformulating the positioning problem as a graph signal recovery task. This approach effectively overcomes signal fluctuation interference in complex topologies like U-shaped corridors, achieving robust position estimation. Experiments demonstrate that this algorithm effectively leverages manifold structure constraints to correct NLOS errors. On real-world field test datasets, compared to traditional weighted algorithms, the average positioning accuracy improves to 1.4267 m, with maximum positioning error reduced by over 50%, achieving high-precision robust positioning. Full article

The coprime array, proposed in recent years as a special type of sparse array, combines the advantages of sparse sensing with the unique properties of prime numbers, enabling a larger array aperture and higher degrees of freedom with the same number of physical sensors. In underwater array signal processing, the high-resolution potential of coprime arrays has attracted significant attention. However, in complex ocean environments, leveraging the advantages of coprime arrays to achieve high-resolution and robust target detection still faces challenges posed by sensor failures. Element failures can disrupt the physical structure of the coprime array, leading to significantly increased energy in grating lobes and side lobes of the beam pattern, thereby raising the probability of false target azimuth identification. To address this issue, this paper analyzes the virtual array set mapped from the physical coprime array and proposes a multiplepath matching pursuit method for underwater vector coprime array target azimuth detection based on random virtual array set construction and verification techniques. Cases of continuous and non-continuous virtual arrays are analyzed, and corresponding solutions are proposed. Through simulations and analyses of sea trial data, it is demonstrated that the proposed method can achieve high-resolution target azimuth detection as well as robust target detection in the presence of physical sensor failures. Full article

Rare-metal pegmatite–greisen systems are commonly small, structurally controlled, and difficult to delineate using conventional mapping alone. This study proposes a multiscale remote-sensing workflow for prospecting Li–Nb–Ta–Cs mineralisation in the Kalba–Narym rare-metal belt (East Kazakhstan) by integrating Sentinel-2 multispectral imagery, UAV-derived centimeter-scale orthomosaics, structural (lineament) analysis, and field-based mineralogical–geochemical validation. Sentinel-2 responses were first calibrated using known occurrences to derive alteration proxies related to greisenisation, silicification, Na-metasomatism, and oxidation. These proxies were combined into an Integrated Hydrothermal Alteration Index (IHAI) to highlight areas where multiple alteration processes overlap. Lineament mapping from Sentinel-2 and DEM products indicates dominant NW–SE and NE–SW structural trends, zones of elevated lineament density and intersection systematically coincide with high IHAI values. UAV orthomosaics refine satellite-scale anomalies by resolving quartz-vein networks, fracture corridors, and surface-alteration textures that are not detectable at 10–20 m resolution. Mineralogical and geochemical data confirm that high-IHAI targets correspond to albitised pegmatites and greisenised rocks enriched in Li, Nb, Ta, and Cs. The results demonstrate that combining freely available Sentinel-2 data with UAV observations and targeted ground validation provides a cost-effective and transferable framework for reducing false positives and prioritising exploration targets in structurally complex granitoid terranes. Full article

Background/Objectives: Despite growing recognition of empathy as a cornerstone of high-quality care, its role within interprofessional collaboration remains underexplored. While the Interprofessional Education Collaborative explicitly references empathy only under the Values and Ethics domain, emerging evidence suggests its potential relevance across all four core competencies. This study aimed to explore the influence of empathy on each of the Interprofessional Education Collaborative core competencies and to highlight its role in the contemporary interprofessional healthcare environment. Methods: A narrative literature review was conducted to identify articles published in English between 2014 and 2025, through searches of PubMed and Scopus. The sub-competency statements of the Interprofessional Education Collaborative framework were used to guide keyword selection and map concepts that empathy may influence. Results: Seventy-two articles were included in this narrative review. According to the literature, evidence suggests that empathy may support humanitarian values and ethical decision making (Values and Ethics), but the mechanisms underlying this remain to be considered. Empathy has also been discussed in relation to therapeutic and team communication (Communication), as well as to processes such as conflict resolution, supportive leadership, team cohesion, and staff well-being (Teams and Teamwork). The evidence regarding the Roles and Responsibilities domain remains relatively limited, preventing definitive conclusions about the potential influence of empathy in this domain. A clear distinction emerges between clinical and interprofessional empathy, with evidence suggesting that both are essential for collaborative practice. Conclusions: Empathy appears to be linked with several domains of interprofessional collaboration and may represent an important relational component in collaborative healthcare practice. However, the influence of empathy may depend on structural and organizational conditions and may vary across different interprofessional healthcare contexts. These findings offer a conceptual bridge between empathy and interprofessional collaboration, providing actionable insights for educators, leaders, and policymakers involved in healthcare training. Full article

Social media has become a prominent digital environment associated with adolescents’ food preferences and the environmental impacts of their diets. This study aimed to examine the relationship between social media usage habits, food choice motivations, and the environmental impact of the diet, specifically the carbon footprint, in adolescents. This cross-sectional study was conducted with 216 adolescents aged 14–18 years in Istanbul between January and April 2025. Data were collected using the Food Choice Questionnaire (FCQ) and a 24 h dietary recall. The dietary carbon footprint was calculated by mapping 24 h dietary recall data to emission factors from the Data FIELDS database and scientific literature. Of the participants, 60.6% were female. Females had significantly higher rates of being influenced by social media in food choices (p < 0.001) and total FCQ scores (p = 0.025) compared to males. Regarding social media platforms, TikTok usage was associated with higher ethical concern and mood scores (p < 0.001), while Instagram usage was associated with weight control (p = 0.012). Daily internet use of 180 min was associated with higher price (p = 0.001) and weight control (p = 0.003) motivations. Notably, a significant negative correlation was found between health motivation and carbon footprint (r = −0.173, p = 0.011). Multivariate regression analysis confirmed that an increase in health score was associated with a reduction in carbon footprint (β = −0.204, p = 0.003), independent of gender, BMI, and social media influence. Social media platforms serve as a relevant digital environment associated with adolescents’ food preferences. The finding that health-oriented choices are associated with lower carbon footprints indicates that promoting healthy eating on social media will benefit both individual and planetary health. Full article

Moyamoya disease (MMD) is a chronic, progressive cerebrovascular disorder characterized by steno-occlusive changes in the intracranial internal carotid arteries and the development of fragile collateral networks. Imaging plays a pivotal role in diagnosis, disease staging, and management, yet the expanding range of available imaging modalities has resulted in heterogeneous evidence that remains difficult to synthesize. This scoping review aimed to systematically map and critically appraise imaging-based diagnostic approaches used in MMD, summarizing their diagnostic performance, clinical utility, and limitations. A comprehensive literature search was conducted across major databases, and original studies evaluating imaging modalities in human MMD were included. Thirty-three studies published between 1995 and 2023 were analyzed, encompassing digital subtraction angiography, magnetic resonance imaging and angiography, perfusion and functional MRI, computed tomography-based techniques, nuclear medicine, ultrasound, neurophysiological methods, and emerging artificial intelligence applications. Digital subtraction angiography remains the diagnostic reference standard, particularly for disease confirmation and surgical planning. However, noninvasive modalities provide critical complementary information. Magnetic resonance-based techniques offer multiparametric assessment of vascular morphology, hemodynamics, vessel wall pathology, and parenchymal injury. Computed tomography angiography and perfusion imaging provide accessible alternatives with high sensitivity for vascular changes, while functional and neurophysiological methods contribute additional hemodynamic and regional assessments. Artificial intelligence applications show promising diagnostic performance but remain in early validation stages. The evidence base is limited by methodological heterogeneity, inconsistent reference standards, incomplete reporting of diagnostic accuracy metrics, and a scarcity of longitudinal and multimodal studies. Collectively, the findings support a multimodal imaging strategy in MMD, integrating structural and functional information to inform diagnosis and management. Future research should prioritize standardized protocols, longitudinal designs, and clinically validated imaging biomarkers to enable evidence-based diagnostic pathways. Full article

Sentinel lymph node mapping is increasingly used in canine and feline oncology and often involves the combined use of visible dyes and fluorescent tracers. However, the effect of methylene blue on the fluorescence of indocyanine green during near-infrared imaging remains unclear. This explorative study aimed to quantitatively and qualitatively assess potential fluorescence quenching in solutions of methylene blue–indocyanine green at different ratios in three near-infrared imaging modalities (overlay, color map, contrast). Four solutions were prepared: 100%/0%, 75%/25%, 50%/50%, and 25%/75% indocyanine green/methylene blue. The fluorescence intensity of the four solutions was quantitatively measured in vitro using near-infrared imaging. Subsequently, four lymphographies, one for each solution, were performed from the metatarsal region of feline cadavers. Observers with varying levels of experience evaluated lymphographic images. Methylene blue caused a concentration-dependent reduction in fluorescence both at the quantitative evaluation and qualitative lymphography interpretation. Despite this reduction, fluorescence remained sufficient in cadavers for accurate identification of lymph nodes, and observer experience did not significantly affect interpretation, except for the color map mode. Because methylene blue-dominant solutions showed a greater quenching effect on indocyanine green fluorescence, clinicians should favor indocyanine green-dominant mixtures. This approach may preserve fluorescence performance, maintaining the surgical guidance benefits of methylene blue. Future confirmatory studies should include a substantially larger number of specimens to allow appropriate statistical comparisons and to better account for inter-individual variability. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Real-time measurement of tea bud phenotypes via mobile devices is constrained by model lightweighting challenges, and research on non-contact measurement of tea bud phenotypes based on key points remains largely unexplored. Information on the growth posture of tea buds is an important basis for determining tea maturity grades, quality monitoring, and tea breeding. Therefore, this work develops a deep learning-enabled YOLOv8p-Tea model to estimate key point information of tea bud posture and automatically obtain three-dimensional point cloud information of tea buds by integrating depth information, thereby achieving in situ measurement of tea bud phenotypic parameters. Meanwhile, the model is trained and validated using a tea bud (one-bud-three-leaf) image dataset, and its effectiveness is demonstrated through experiments. Compared to the YOLOv8p-pose model, the model achieves a mAP50 of 98.3%, a P of 97%, and parameters of 0.72 M, with mAP50 and P improved by 1.5% and 1.9%, respectively, and the parameter count is reduced by 25%. To validate the accuracy of phenotypic extraction, the model was deployed on edge devices, and 30 tea buds with one bud and three leaves were randomly selected in a tea garden. The final in situ measurement results showed an MRE of 6.63%. Experimental findings indicate that the developed method is capable of not only effectively estimate tea bud posture but also accurately achieves in situ measurement of tea bud phenotypes, which holds potential applications for meeting the construction needs of smart tea gardens and optimizing tea breeding. Full article

The European Environment Agency believes that circular economy strategies could substantially contribute to CO₂ emissions reduction. Therefore, it is necessary that the agro-industrial sector identifies sustainable technologies for side-stream management. The scope of this review was to compare the sustainability of available biological treatments for by-product biomasses and organic waste. A total of 147 studies, all Life Cycle Assessments (LCAs) and Techno-Economic Analyses (TEAs), were selected through PRISMA-ScR methodology, on Scopus and Web of Science, and were bibliographically mapped on VOSviewer (Version 1.6.20) Anaerobic digestion and integrated energy recovery systems were found to be the most environmentally robust options. Integrated biorefineries and multi-product systems have emerged as the highest long-term sustainability potential, especially when process integration and co-product recovery were also implemented. Importantly, the most sustainable systems were found to have required considerable start-up investments. Thus, sustainable deployment of biological treatment technologies was clearly dependent on time-consistent policy frameworks that have been fertile to capital-intensive infrastructures via incentives and fiscal measures and that have embraced circular bioeconomy systems. Finally, this paper has demonstrated that the sustainability of biological treatments has resulted from optimal relationships between biomass characteristics, system boundaries, process integration, and market value of co-product, while no single technology has been sufficient in isolation. Full article

Kyoho grape, a leading table grape variety in China, is prone to rapid postharvest deterioration due to its soft texture and high respiration rate. Despite the use of low-temperature storage and modified atmosphere packaging (MAP), systematic studies defining the optimal combination of subzero temperature and gas composition for Kyoho grapes remain lacking. This study aimed to fill this gap by evaluating the synergistic effects of subzero temperature and MAP on quality preservation. Results demonstrated that storage at −1 °C most effectively maintained fruit firmness, stem freshness, and key biochemical components. Based on this temperature, a gas composition of 3% O₂, 15% CO₂, and 82% N₂ was identified as the most effective, extending postharvest shelf life to 54 days. Additionally, a kinetic shelf-life prediction model based on firmness changes was developed with relative errors below 10%, demonstrating high accuracy. This study establishes an integrated preservation strategy combining subzero temperature (−1 °C) and optimized MAP (3% O₂, 15% CO₂, 82% N₂) that significantly extends the shelf life of Kyoho grapes, providing a practical solution for enhancing postharvest quality. Full article