Search Results (25,137)

Parameters of reflected light, measured in narrow or broad spectral bands, are widely analyzed for remote and proximal sensing of plant responses to stressors. Specifically, parameters of reflectance in red (R), green (G), and blue (B) spectral bands measured using simple color images can be sensitive to characteristics of plants. The conventional view is that RGB reflectance primarily reveals long-term changes in plants (days, weeks, etc.). In this study, we investigated light-induced changes in RGB reflectance in wheat (Triticum aestivum L.) and pea (Pisum sativum L.) leaves. Illumination increased this reflectance for about 10 min in wheat and about 15–20 min in pea; these changes relaxed after light intensity was decreased. The changes in RGB reflectance were strongly related to the effective quantum yield of photosystem II and non-photochemical quenching of chlorophyll fluorescence under high light intensity; these relations were absent under low light intensity. We hypothesized that changes in both RGB reflectance and photosynthetic parameters were related to the light-induced changes in chloroplast localization. A simple mathematical model of optical properties and photosynthesis in leaves was developed; results of the model-based analysis supported the proposed hypothesis. Experimental analysis of the dynamics of light transmittance additionally supported this hypothesis. Our results thus show that RGB imaging can be sensitive to fast changes in plants. Full article

Optimizing nitrogen (N) management is crucial for high-quality rice (Oryza sativa L.) production. However, how N affects grain quality at different positions within a panicle remains unclear. This study evaluated the effects of different N application regimes on the milling, appearance, eating, and nutritional quality of grains at varying panicle positions. We used a japonica cultivar Wuyunjing 31 in a controlled pot experiment with three N treatments: N32:0 (early heavy N), N16:16 (split application with late N topdressing), and N16:0 (low-N control). Results showed that late N topdressing (N16:16) significantly improved head rice yield across all grain positions, which was linked to higher storage protein accumulation (especially glutelin) and larger length-to-width ratio. Conversely, late N application deteriorated appearance quality by increasing the chalky grain rate and chalkiness. This negative effect was most pronounced in superior grains on upper and middle branches. Furthermore, the N16:16 treatment consistently decreased amylose content while increasing albumin, prolamin, and glutelin levels, demonstrating a clear trade-off between carbon (C) and N sinks. We speculated that these intra-panicle differences result from increased competition for carbon resources between starch and protein synthesis pathways. Overall, precision N management should account for spatial differences in grain development to effectively balance rice yield and quality. Full article

Background/Objectives: Amniotic-derived biologics have emerged as powerful modulators of tissue regeneration. This study evaluates the composition and characteristics of a human stem cell-conditioned media (hSCCM) that is a sterile, cell-free, amniotic-derived solution, and the presumed efficacy of hSCCM as an active ingredient in an enriched cosmetic lotion. Methods: Data from preclinical benchtop studies and an exploratory observational assessment were reviewed. First, an investigation of the active ingredient, hSCCM, was completed. Flow cytometry assays were completed for mesenchymal stem cell (MSC) characterization. Cellular proliferation assays were conducted to evaluate concentration response, shelf life, and temperature stability. ELISA and LC-MS/MS were used to specify and detail the proteomics of the hSCCM. Second, the hSCCM-enriched lotion’s cosmetic safety and efficacy were evaluated. Preliminary microbial, stability, and early-stage nonclinical retrospective user evaluation of the hSCCM-enriched lotion was conducted to help characterize the cosmetic and evaluate topical safety and efficacy. Results: Flow cytometry demonstrated alignment with ISCT (International Society for Cell and Gene Therapy) characterization for MSCs. Initial in vitro data demonstrated enhanced proliferative effects at hSCCM concentrations as low as 5% (p-value < 0.0001); no statistically significant trend in proliferative capability in aged samples (p-value = 0.79), and no significant effect on proliferative capability when exposed to acute temperature changes (p-values all above 0.05) were observed. Proteomic characterization showed an enriched amniotic-derived solution. Microbial testing of the enriched lotion demonstrated success with multiple unique preservative formulations. hSCCM-enriched lotion demonstrated stability across acute cold- and heat-stress representative scenarios. An exploratory retrospective observational analysis revealed promising trends. Conclusions: The hSCCM demonstrates topical efficacy across in vitro dermal and follicular assays via proliferative and regenerative mechanisms and protein enrichment. The enriched lotion showed success in early-stage microbial and stability testing and demonstrates positive trends in topical skin outcomes. These findings support their potential translational application in dermatologic and aesthetic usage, and broader integumentary contexts. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

This study investigates the acoustic emission (AE) response and damage mode characteristics of ±45° glass fiber-reinforced polymer (GFRP) composites used in wind turbine blade shear webs under quasi-static tensile loading. It aims to establish the relationship between AE features and three typical damage mechanisms—matrix cracking, interfacial debonding, and fiber fracture—to support damage assessment and structural health monitoring. Quasi-static uniaxial tensile tests with synchronous AE monitoring are conducted on specimens with three orientations (0°, 45°, and 90°). AE features are selected using correlation analysis and principal component analysis, and the HAC-initialized K-means clustering method is employed for damage mode identification. The optimal number of clusters is determined to be three, according to the Davies–Bouldin index (DBI) and the Silhouette index (SI). The resulting low-, mid-, and high-frequency clusters are associated with matrix cracking, interfacial debonding, and fiber fracture, respectively. These interpretations are further supported by wavelet-based time–frequency analysis and microscopic fracture surface observations. Full article

Intensive ornamental planting is increasingly prevalent in urban green spaces, yet its effects on soil microbial community assembly and interaction networks remain poorly understood. Here, we examined shifts in soil properties, microbial diversity, community composition, and interaction networks across successive planting cycles. Bacterial alpha-diversity remained relatively stable, whereas fungal communities showed pronounced sensitivity to early planting stages. Beta-diversity analyses revealed that bacterial community composition was jointly influenced by planting stage and site type, while fungal communities were primarily structured by site characteristics. Co-occurrence network analysis revealed contrasting reassembly trajectories between microbial groups. Bacterial networks exhibited increasing complexity and modularity, indicating enhanced interaction intensity and competitive structuring under intensive management. In contrast, fungal networks displayed reduced connectivity but maintained or recovered modular organization, suggesting structural buffering. Notably, keystone taxa remained taxonomically conserved, indicating that network reorganization was driven by interaction rewiring rather than species turnover. We propose a dual-speed reassembly framework in which bacteria function as fast-responding components with dynamic interaction networks, whereas fungi act as slow-buffering, structurally persistent elements. This decoupling of short-term functional responsiveness and long-term stability provides new insights into how intensive management reshapes soil microbiomes in botanical garden ecosystems. Full article

With the rapid development of Intelligent Transportation Systems (ITSs), traffic prediction, a crucial component of ITSs, has garnered growing scholarly attention. The appli-cation of deep learning into traffic prediction has emerged as a prominent research direction, especially amid the rapid advancement of pretrained large language models (LLMs), which offer substantial benefits in time-series analysis through cross-modal knowledge transfer. In response to this advancement, this study introduces an innovative model for traffic flow prediction, designated as WGLLM. To capture spatiotemporal characteristics inherent in traffic flow data, this model incorporates a sequence embedding layer constructed on the stationary wavelet transform (SWT) and long short-term memory (LSTM), in conjunction with a spatial embedding layer founded on graph convolutional networks (GCNs). Additionally, a fully connected layer is utilized to integrate embeddings into the LLMs for comprehensive global dependency analysis. To verify the effectiveness of the proposed approach, experiments were carried out on two real traffic flow datasets. The experimental results demonstrate that WGLLM achieves superior predictive performance compared to multiple mainstream baseline models, accompanied by a significant enhancement in prediction accuracy. Full article

This study explores how residents of Vrnjačka Banja (Serbia) perceive the impacts of tourism and how these attitudes influence their support for future tourism development. Specifically, it examines positive and negative economic, socio-cultural, and environmental impacts, as well as the types of tourism residents favor. Data were collected from 420 local residents using a structured survey, and the reliability of the scales was confirmed using Cronbach’s alpha. Descriptive statistics provided an overview of participant characteristics, while MANOVA and follow-up ANOVA tests were used to examine differences in perceived tourism impacts across tourism types. Multiple linear regression was used to assess how attitudes toward positive and negative impacts predict residents’ support for future tourism development. The results indicate that attitudes toward positive impacts are relatively consistent across residents, whereas negative socio-cultural and environmental impacts differ depending on the type of tourism they support. Regression analysis shows that positive socio-cultural and environmental impacts are the strongest drivers of residents’ support, while negative socio-cultural and economic impacts reduce support. These findings highlight the importance of social and environmental considerations in shaping community attitudes and suggest that sustainable tourism planning should prioritize local well-being and responsible environmental management alongside economic objectives. This study contributes to the literature by addressing the heterogeneity in residents’ attitudes through tourism type preferences, while also highlighting the limited research on this topic in spa destinations. It further provides practical guidance for destination managers and policymakers in developing more targeted and sustainable tourism strategies. Full article

Conventional fault analysis and calculation methods developed for synchronous-generator-dominated power systems exhibit limited applicability to distribution networks with high penetration of distributed generation (DG). These methods cannot provide a reliable theoretical basis for fault characteristic analysis or protection coordination. Existing studies on this scenario have primarily focused on the integration of grid-following (GFL) inverter-based resources (IBRs). By contrast, research on the integration of grid-forming (GFM) IBRs—particularly the virtual synchronous generator (VSG), which enables stable and sustainable utilization of renewable energy resources as synchronous generators—remains significantly inadequate. Therefore, this paper introduces a concise fault analysis and calculation method tailored to distribution networks with VSG-type DGs. First, the control strategy of the VSG-type DGs is examined, and the active-power response characteristics of VSG-type DGs are analyzed for faults in distribution networks. Equivalent models of a typical distribution network with VSG-type DGs are then established for symmetrical and asymmetrical faults. Subsequently, leveraging the active power–frequency and reactive power–voltage dependencies, a fault calculation method for distribution networks is proposed and its generality is examined. The method is convenient to implement and computationally efficient. It requires no detailed information on internal PI controller parameters or time constants of the control loops. PSCAD/EMTDC simulations are performed to verify the high accuracy and suitability of the proposed method for multi-DG scenarios, which facilitates the integration of VSG-type DGs in distribution networks. Full article

Background: Growth differentiation factor-15 (GDF-15) is a stress-responsive cytokine associated with inflammation, metabolic dysfunction, and cardiovascular disease. Its role as a biomarker of microvascular complications in type 2 diabetes (T2D) remains incompletely defined. Objective: To evaluate circulating GDF-15 levels and their association with microvascular complications in patients with T2D. Methods: This cross-sectional study included 160 participants divided into three groups: T2D (n = 93), obesity without carbohydrate disorders (n = 36), and healthy controls (n = 31). Microvascular complications (neuropathy, nephropathy, retinopathy) were assessed. Multivariable logistic regression and receiver operating characteristic (ROC) analysis were performed. Results: GDF-15 levels were significantly higher in T2D compared with non-diabetic individuals (267.5 ± 168.9 vs. 118.3 ± 55.5 pg/mL, p < 0.001). Higher GDF-15 was associated with neuropathy (odds ratio (OR) 1.985; 95% confidence interval (CI) 1.431–2.753) and nephropathy (OR 1.673; 95% CI 1.243–2.254) in unadjusted models. After adjustment, only nephropathy remained independently associated (OR 1.405; 95% CI 1.026–1.923). ROC analysis showed moderate discriminative ability for nephropathy (area under the curve (AUC) = 0.763). Conclusions: Circulating GDF-15 levels are significantly elevated in patients with T2D and are associated with microvascular complications, with the strongest independent association observed for diabetic nephropathy. These findings suggest that GDF-15 may represent a promising biomarker reflecting metabolic stress and risk of diabetic complications. Full article

Background/Objectives: Bovine viral diarrhea (BVD) is a major infectious disease of cattle caused by bovine viral diarrhea virus genotypes 1 and 2 (BVDV-1 and BVDV-2). Current inactivated and live attenuated vaccines provide incomplete cross-genotype protection and may exhibit limitations related to durability of immunity or safety. This study evaluated whether co-expression of the BVDV envelope glycoproteins E1 and E2 in a Modified Vaccinia Ankara (MVA) vector could support antigen expression and induce immune responses in a proof-of-concept model. Methods: Recombinant Modified Vaccinia Ankara (MVA) viruses expressing BVDV-1 E1E2 or BVDV-2 E1E2 were generated by homologous recombination. Recombinant viruses were purified and characterized for antigen expression, genetic stability, and growth properties in vitro. Immunogenicity was evaluated in a BALB/c mouse model by measuring E2-specific antibody responses, virus-neutralizing antibodies, and antigen-responsive cellular immune responses. Results: Both recombinant MVA constructs showed detectable E2 expression when E1 and E2 were co-expressed, and exhibited growth characteristics comparable to parental MVA with stable maintenance after serial passage. In contrast, recombinant MVA expressing E2 alone did not yield detectable E2 protein under the same experimental conditions. Immunization induced detectable humoral and cellular immune responses, including E2-specific IgG antibodies, virus-neutralizing antibodies, and increased frequencies of antigen-responsive CD8⁺ T cells with a tendency toward a Th1-biased profile. Conclusions: These findings indicate that co-expression of BVDV E1 and E2 in an MVA vector can support detectable antigen expression and induce measurable immune responses in a mouse proof-of-concept model. Further studies in cattle, including challenge experiments, will be required to determine the protective efficacy and practical applicability of this platform for BVDV vaccine development. Full article

To systematically investigate the combined effects of moisture content, confining pressure, and loading rate on the mechanical properties of weakly cemented soft rock, this study focuses on the Jurassic coal measures from the Hoxtolgay coalfield in Xinjiang. A series of uniaxial and triaxial compression tests were conducted under varying moisture states, loading velocities, and confining pressures. Complementary X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brazilian splitting tests were performed to analyze the microstructural evolution and tensile failure characteristics. The experimental results demonstrate that moisture content acts as the primary governing factor for mechanical degradation; increased hydration promotes clay mineral swelling and attenuates inter-granular cementation, leading to a continuous reduction in both compressive and tensile strengths, as well as the elastic modulus. Conversely, confining pressure consistently enhances these macroscopic mechanical parameters by restricting lateral deformation. While the loading rate alters the mechanical response, its impact is secondary compared to the definitive effects of moisture and stress constraints. Furthermore, by utilizing established stress–strain-based indices, the study quantitatively evaluates the brittleness characteristics, confirming that hydration fundamentally drives the rock mass from a brittle state toward ductility. This research elucidates the coupled degradation mechanisms of highly sensitive soft rock, providing a theoretical foundation for stability design and risk assessment in underground geotechnical engineering. Full article

Cold-active cellulases are highly desirable for temperature-sensitive biomass valorization and food processing, yet they remain scarce in conventional industrial fungal platforms. In this study, a novel cold-induced cellobiohydrolase, VvCBHI-II, was mined from the mushroom Volvariella volvacea and successfully engineered into the industrial workhorse Trichoderma reesei via site-specific homologous replacement. Structural homology modeling revealed that the substitution of the flexible B3 loop with a β-sheet creates a more open substrate-binding cleft in VvCBHI-II. Consequently, the purified VvCBHI-II exhibited robust endoglucanase-like characteristics with superior catalytic efficiency on amorphous cellulose. At 10 °C, the engineered cellulase complex demonstrated an 8.1-fold increase in filter paper activity compared to the wild-type strain. Mechanistic structural analyses indicated that the open cleft architecture elongates and weakens the hydrogen-bonding network with the cellobiose product, facilitating rapid product dissociation and alleviating severe cold-induced product inhibition. In practical applications, the engineered cold-active enzyme complex exhibited an exceptional saccharification capacity on natural pear pomace at 10 °C. Furthermore, when applied to simulated fruit juice processing, it significantly maximized the extraction yield, elevated the sweetness response, and substantially mitigated undesirable bitterness and astringency. This study elucidates the structural-functional paradigm of cold-adapted cellobiohydrolases and provides a promising strategy for formulating highly efficient, energy-saving biocatalysts for the food and biorefinery industries. Full article

Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer mortality, largely due to late diagnosis, frequent relapse, and the emergence of chemoresistance. An important but often-overlooked contributor to treatment failure is the heterogeneous penetration of anticancer drugs within tumors. Structural and biochemical barriers—including abnormal vasculature, elevated interstitial pressure, dense extracellular matrix, drug efflux transporters, and malignant ascites—generate steep intratumoral concentration gradients that conventional preclinical models fail to capture. As a result, systemic pharmacokinetic measurements frequently provide limited insight into tumor-level drug exposure. Patient-derived organoids (PDOs) have emerged as physiologically relevant 3D models that preserve the genetic, architectural, and functional characteristics of the original tumor. These systems enable controlled investigation of pharmacokinetic and pharmacodynamic processes, including drug penetration, metabolism, retention, and exposure–response relationships. Adding cell-free malignant ascites supernatant enhances PDOs’ ability to mimic the metastatic peritoneal microenvironment of OC. This review discusses recent advances in PDO technologies and examines how PDO-derived data can inform intratumoral pharmacokinetics and dosing strategies using physiologically based pharmacokinetic modeling and in vitro–in vivo extrapolation. Emerging hybrid platforms, including organoid-on-chip systems, vascularized co-cultures, and multi-omics integration, are crucial to improve translational prediction and support precision oncology. Full article

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article