Search Results (28,011)

Autophagy plays a central role in cellular homeostasis and metabolic adaptation, and its dysregulation has been implicated in metabolic disorders, including non-alcoholic fatty liver disease (NAFLD). This study investigated the biological effects of a chemically characterised multi-component nutraceutical formulation using an integrated in vitro gut–liver–muscle axis model under lipotoxic and inflammatory conditions induced by free fatty acids (FFAs) and lipopolysaccharide (LPS). The principal bioactive constituents were quantified in both the individual extracts and the final formulation before biological testing. Caco-2, HepG2, and C2C12 cells were sequentially exposed to conditioned media to reproduce inter-organ metabolic interactions. The Supplement preserved intestinal barrier integrity by maintaining transepithelial electrical resistance and tight junction protein expression. In HepG2 cells, it preserved telomerase levels, improved markers of cellular metabolic adaptation, modulated AMPK/mTOR and SIRT1 signalling, and promoted autophagy-related responses, including increased LC3-II/I ratio, reduced p62 accumulation, and preservation of lysosomal markers. In skeletal muscle cells, exposure to conditioned medium derived from formulation-treated compartments was associated with improved cellular bioenergetics, reduced oxidative stress and inflammatory mediators, and enhanced ATP and glycogen levels under exercise-like conditions. Overall, these findings provide preliminary evidence that the chemically characterised formulation modulates interconnected pathways involved in intestinal barrier function, hepatic autophagy-related processes, and skeletal muscle metabolic adaptation under the experimental conditions employed. Full article

The implementation of collaborative robots (cobots) in Industry 5.0 requires a synergistic connection between technical safety and integrated management systems (IMSs). This article introduces three newly defined interaction concepts that map technological evolution: HCDI, an approximation for Industry 4.0; HCD_XI, designed for anthropocentric Industry 5.0; and aICD_XI, which anticipates the emerging cognitive era of artificial intelligence. The principal contribution is an original four-phase engineering algorithm for cobot implementation linked to thirteen ISO standards, including ISO 9001, ISO 45001, ISO 14001, ISO/IEC 27001, and ISO/IEC 42001. The algorithm applies the multicriteria SCATI method for process prioritization and the probabilistic reliability performance index (RPI) based on conditional probabilities as an operational reliability acceptance gate, distinct from ISO/TS 15066 functional safety requirements. IMS synergies are visualized by the Synergy HOUSE architectural model. The methodology was validated in an optical quality-inspection cell consisting of MiR100 + UR5e + Robotiq + Smart Vision ADMIN 4.0. Evaluated via Monte Carlo simulation, the pre-operational index reached RPI = 0.9801. Deployment of the node demonstrated a 60.0% increase in productivity, a 64.5% decrease in the defect rate, a statistically significant 32.0% reduction in cognitive workload, and a 50.0% reduction in total energy consumption (ISO 50001), enabled by the transition to lights-out operation. At the same time, the energy paradox of edge cooling for AI was described. The results confirm the robustness of the algorithm for systematic and sustainable enterprise transformation. Full article

Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods: We analyzed 212 hospitalized children and adolescents receiving inpatient care for a major depressive episode. All had a Nurses’ Global Assessment of Suicide Risk (NGASR) rating, self-report measures, covariates, and first admission-night non-contact autonomic data. Principal component analysis was used to derive a nocturnal autonomic dysregulation factor from heart-rate and heart-rate-variability (HRV) summaries. Ordinary least squares regression with HC3 robust standard errors estimated its concurrent association with NGASR after adjustment for age, sex, BMI, Beck Depression Inventory score, and Chinese Beck Scale for Suicide Ideation score. Score-appropriate sensitivity analyses used Poisson, negative-binomial, and predefined NGASR-category models. Results: Higher autonomic dysregulation was associated with higher NGASR after adjustment for depressive symptoms and self-reported suicidal ideation (standardized beta = 0.182, 95% CI 0.067 to 0.297; p = 0.002; q = 0.005), accounting for an additional 3.2 percentage points of explained variance. The association was similar in a robust Poisson model (incidence-rate ratio = 1.059, 95% CI 1.022 to 1.097; p = 0.001) and remained stable after adjustment for subjective sleep quality, AHI, sleep efficiency, and monitoring timing. Separate component models showed larger associations for SDNN and RMSSD than for heart rate or LF/HF, but did not decompose the composite effect. Conclusions: Admission-window nocturnal autonomic dysregulation showed a modest concurrent association with clinical suicide-risk assessment after adjustment for depressive symptoms and self-reported suicidal ideation. The finding does not establish disclosure-independent assessment, temporal improvement of admission assessment, or prediction of future suicidal behavior. Full article

Background/ Objectives: Selecting an intraoral scanner (IOS) has evolved into a complex clinical decision that extends beyond technical performance alone. This study aimed to investigate the clinical, technical, educational, and organizational factors influencing intraoral scanner selection among dentists, evaluate awareness of ISO specifications and evidence-based criteria, and propose a conceptual framework to support evidence-based technology selection. Methods: A cross-sectional questionnaire-based study was conducted among dentists practicing in Greece. A total of 86 questionnaires were returned from 271 invited participants (response rate: 31.73%). Valid sample sizes varied across analyses according to item applicability and analyzable responses. The questionnaire assessed demographic and professional characteristics, intraoral scanner use, selection criteria, awareness of ISO specifications, educational background, and evidence-related factors. Composite indices were developed to evaluate the principal decision-making dimensions. Results: Technical and clinical performance-related criteria received the highest importance ratings in intraoral scanner selection. Although participants generally reported good perceived knowledge of intraoral scanners, only 35.7% reported awareness of ISO specifications. Dentists familiar with ISO standards assigned significantly greater importance to specification-related and safety-related criteria, suggesting an association between reported ISO awareness and greater emphasis on specification- and safety-related selection criteria. Continuing professional education represented the predominant source of knowledge acquisition. Based on the integration of these findings, the EBIOS Pilot Framework (Evidence-Based Intraoral Scanner Selection Framework) is proposed as a preliminary conceptual synthesis of factors potentially relevant to evidence-informed intraoral scanner selection. Conclusions: The findings suggest that intraoral scanner selection may be understood as a multidimensional decision-making process involving technical performance, scientific evidence, professional education, and standardized quality criteria. The proposed EBIOS Pilot Framework provides a preliminary conceptual basis for future validation and refinement in larger, independent populations. Full article

Maintenance planning for highway bridges without historical inspection records remains challenging because conventional deterioration models typically require long-term data for calibration. This study proposes an inspection-driven decision-support framework that integrates bridge-specific engineering calibration, Markov deterioration modelling, an independent condition-rating-based Remaining Service Life (RSL) assessment, and Markov Decision Process (MDP) optimization. The framework was demonstrated on a 26-year-old six-span composite highway bridge in Türkiye. A comprehensive inspection yielded a weighted Bridge Condition Index of 2.98, which was used to calibrate the bridge-specific Markov deterioration model. The model predicted attainment of the State-4 intervention threshold after approximately 15.71 years under a do-nothing scenario, while the independent condition-rating assessment estimated an RSL of approximately 17 years for the governing pier columns. The optimized finite-horizon MDP policy reduced the expected discounted life-cycle cost by 89.75% relative to the do-nothing strategy, while sensitivity analyses confirmed the stability of the principal maintenance policy under the examined modelling and economic perturbations. The proposed framework therefore provides a practical, transparent, and progressively updateable methodology for deterioration prediction and maintenance planning for bridges with limited historical inspection information. Full article

To enable rapid, non-destructive identification of damage to Yali pears, this study proposes a detection method that integrates short-wave infrared hyperspectral imaging (1000–2500 nm), regularised linear discriminant analysis (R-LDA) and a lightweight convolutional neural network (CNN). The experiments utilised 180 Yali pears (60 each of healthy, with Mechanical scratch and Compression damage specimens) as training samples, whilst a further 300 independent fruits (150 healthy and 150 damaged) were used for fruit-level sorting validation. Following pre-processing using Principal Component Analysis (PCA) to eliminate multicollinearity, the classification performance of three feature extraction strategies—PCA, Independent Component Analysis (ICA) and R-LDA—was compared when combined with the same lightweight CNN. The results indicate that R-LDA’s Fisher discrimination criterion (5.0275) and separation index (2.3543) were both superior to those of PCA and ICA, and its dimension-reduced features exhibited stronger inter-class separability. In pixel-level testing, the R-LDA + lightweight CNN achieved recognition accuracies of 98.60 per cent and 99.32 per cent for Compression damage and background, respectively, and 93.54 per cent and 96.33 per cent for Mechanical scratch and intact tissue, respectively. In a validation study involving the sorting of 300 independent fruits, this method achieved a recall rate of 100.00% for damaged fruits (zero false negatives), with precision and F1 scores of 97.00% and 97.09% respectively, both of which outperformed PCA combined with a lightweight CNN and ICA combined with a lightweight CNN. The above results indicate that the combination of R-LDA discriminant dimensionality reduction and a lightweight CNN can effectively reduce redundancy in hyperspectral data whilst maintaining a high damage detection rate, thereby providing a viable solution for the rapid, non-destructive detection of post-harvest damage in Yali pears. Full article

The quality and flavor of vine tea are largely determined by its processing stages, which markedly influence its physical attributes and volatile organic compounds. Elucidating the dynamic changes in physicochemical and aromatic properties throughout processing is, therefore, essential for guiding optimized processing techniques and developing high-quality vine tea products. However, the specific effects of individual processing stages on quality attributes remain poorly understood. In this study, we combined assessments of color and physical properties with untargeted metabolomics, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), relative odor activity value (ROAV), and gas chromatography–olfactometry (GC-O) to identify key compounds contributing to vine tea quality. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were further applied to identify characteristic metabolites associated with aroma and flavor differentiation. A total of 280 volatile organic compounds were identified, among which 14 key VOCs (ROAV ≥ 1, aroma intensity ≥ 0.5) exhibited significant dynamic variation across processing stages. Notably, compounds such as β-ionone, β-myrcene, nonanal, and hexanal displayed higher ROAVs and strong aroma intensities (AI ≥ 1.0), indicating their substantial contribution to overall aroma. Furthermore, the metabolic transformation pathways—primarily including fatty acid degradation and carotenoid cleavage—and the content changes of key aroma-active compounds were inferred across different processing stages. Based on the differential accumulation patterns of the identified volatile markers, the possible involvement of fatty acid degradation and carotenoid cleavage pathways was inferred. Drying and fixation emerged as critical steps for vine tea aroma development, while the non-enzymatic degradation of fatty acids was potentially associated with the formation of its aroma characteristics. This study provides insights that may inform future efforts toward processing optimization and quality improvement of vine tea. Full article

Remote sensing is widely used in archaeology, but the lack of standardised and readily deployable preprocessing workflows limits reproducibility and cross-study comparability, particularly for very high-resolution multispectral imagery. This study presents ArqPy, a Python toolbox designed to automate and standardise image preprocessing, enhancement and initial interpretation for archaeological prospection. The toolbox includes atmospheric correction, conventional pansharpening, spectral indices, principal component analysis (PCA), and spatial filtering. Its object-oriented architecture currently supports WorldView-3 (WV3) and WorldView Legion (LEGION) imagery and facilitates the future integration of additional sensors. ArqPy also incorporates complementary AI-based tools: Masked Autoencoder (MAE) feature analysis for exploring and ranking derived products, Z-PNN for deep-learning-based pansharpening, and SAM 3 for text-guided segmentation of candidate crop marks. The toolbox was applied to the preliminary inspection of crop marks at the Zar Tepe archaeological site in southern Uzbekistan before the 2026 field campaign. This case study illustrates the proposed workflow and demonstrates how ArqPy provides a reproducible and readily deployable environment through which archaeologists can access advanced image-processing methods. AI-based tools can support preliminary reconnaissance, but they cannot replace expert interpretation without further training and validation using site-specific archaeological data. Full article

Background/Objectives: Dietary nitrate, long framed through food-safety concerns about N-nitroso compound formation, is now also recognised as a substrate of the nitrate–nitrite–nitric oxide pathway. This review aims to define the mechanistic, dietary and host conditions under which nitrate bioactivation becomes functionally relevant, with particular attention to its microbial determinants and to the level of inference the evidence actually supports. Methods: We conducted a narrative review supported by a structured literature search (PubMed, Scopus and Web of Science; 1 January 1976 to 14 February 2026; full-text, peer-reviewed, English-language, human-relevant sources; 148 sources retained, of which 93 contributed to the evidence synthesis), with narrative synthesis of mechanistic, interventional, observational and regulatory sources addressing dietary source and food matrix, enterosalivary metabolism, oral and gut microbial function, and health-related outcomes. A PRISMA-style flow diagram summarises the documented screening and inclusion process, and the complete database-specific search strategies are provided in ^{Supplementary Table S1}; no meta-analysis was performed because of substantial heterogeneity in designs and outcomes. Results: Within the canonical enterosalivary pathway, nitrate-to-nitrite bioactivation is predominantly microbiota-dependent and downstream conversion is chemically conditional: within the enterosalivary cycle, nitrate-reducing bacteria on the tongue dorsum generate the nitrite required for downstream nitric oxide formation, and its conversion in the stomach depends on pH and on matrix constituents. Dietary source and food matrix therefore govern both the delivered dose and the chemistry that follows, so vegetables, beetroot products, inorganic salts, drinking water and processed meat are not interchangeable exposure models. The oral microbiota is the principal microbial determinant of the response, whereas the gut microbiota acts as a context-dependent modifier of intestinal redox tone, barrier function and microbial ecology, supported by markedly weaker human evidence. Nitrate-rich sources reproducibly raise nitrate and nitrite biomarkers, with variable effects on blood pressure, vascular function and exercise efficiency, limited or inconsistent effects on cognition, cerebral blood flow and metabolic endpoints, and a safety profile whose interpretation depends on food matrix, dose, exposure pattern and host context rather than concentration alone. Conclusions: We propose the Source–Matrix–Microbiota–Host (SMMH) framework, in which biological impact depends on the interaction between dietary source and dose, food matrix, microbial nitrate-reducing capacity and host susceptibility, rather than on nitrate dose alone, and in which pathway-level, physiological and clinical evidence are kept explicitly distinct. The evidence base is mechanistically robust for the oral microbiota, considerably less defined for the gut microbiota, and variable at the level of validated clinical endpoints; it does not yet support source-independent guidelines or population-level recommendations. Full article

Livestock manure is both an environmental burden and potential feedstock for the circular bioeconomy and sustainable biorefinery systems. This study estimated the theoretical potential of livestock manure valorisation using a balanced 50-country panel from 2000 to 2023, with illustrative scenario projections to 2050. Livestock stock data and population data were combined with species-specific coefficients to estimate manure production, standardised theoretical resource CH₄ potential, standardised theoretical resource gross methane energy potential, standardised theoretical resource nitrogen and phosphorus potential, and the recoverable-CH₄ CO₂e value. Country rankings, species contribution analysis, k-means clustering, principal component analysis and ARIMA-based scenario analysis were used to compare manure-resource indicators. Between 2000 and 2023, total estimated manure production increased from 29.25 to 33.18 Gt, while standardised theoretical resource gross methane energy potential increased from 3396.6 to 3977.8 TWh. Standardised theoretical resource nitrogen, standardised theoretical resource phosphorus and recoverable-CH₄ CO₂e value also increased, whereas mean standardised theoretical resource gross methane energy potential per capita declined. In 2023, India, Brazil, China, the USA and Pakistan had the greatest total standardised theoretical resource gross methane energy potential, while Uruguay, New Zealand, Paraguay, Ireland and Argentina had the highest per capita gross methane energy potential. Cattle dominated the estimated manure resource, contributing 76.1% of total manure production. The 2050 scenario values were derived from an exploratory ARIMA trajectory based on 24 annual observations and should be interpreted as model-dependent sensitivity outputs, not as strong long-term forecasts. Under low, medium and high illustrative adoption assumptions, scenario-adjusted 2050 values were 8.04, 5.81, and 2.28 Gt CO₂e, respectively. The study provides standardised theoretical manure-resource indicators for comparative screening, rather than country-specific feasibility estimates or implementation forecasts. Full article

Dryland degradation increasingly compromises ecosystem stability, food production, and regional development. Clarifying its long-term evolutionary patterns and regional variations is therefore essential for formulating targeted management strategies. Leveraging GEE, we assembled multiple remote-sensing products together with land-cover information to establish a 1-km-resolution assessment framework describing vegetation conditions, drought pressure, potential soil salinity, and the ecological status associated with different land-cover types. An entropy-based weighting scheme was subsequently employed to derive the land degradation risk index (LDRI). This index enabled an assessment of changes in land degradation risk across arid Northwest China over the period 2001–2024, while also allowing the relative contributions of the principal driving factors to be evaluated. The analysis indicated that degradation risk generally weakened throughout the region, and shifts among risk categories occurred predominantly through stepwise movement between neighboring levels. Areas shifting toward lower-risk classes accounted for 75.36% of the total area experiencing risk-class changes. Nevertheless, High risk areas continued to be concentrated in the central-western desert belt, while the overall spatial pattern showed limited variation. Driver analysis indicated that vegetation productivity and hydrothermal conditions had relatively high explanatory power, while interactions among factors generally exhibited enhanced effects. Further stratified analysis showed that land degradation risk in vegetation zoning was mainly controlled by moisture conditions and vegetation productivity, whereas hydrothermal conditions exerted a stronger influence in non-vegetation zoning. The proposed LDRI provides a distinct analytical framework for the long-term monitoring and spatially differentiated management of land degradation risk across arid Northwest China, with broader implications for the scientific assessment and management of dryland degradation. Full article

The characterization of plant-derived extracts is strongly influenced by the extraction procedure and solvent polarity, which determine the recovery of bioactive compounds and volatile constituents. In this study, an electronic nose (E-nose) based on a metal oxide semiconductor (MOS) sensor array was evaluated as a rapid analytical tool for the classification of Cistus extracts obtained using solvents of different polarity. Leaves of Cistus salviifolius, Cistus crispus, and Cistus ladanifer were sequentially extracted with hexane, diethyl ether, tetrahydrofuran (THF), chloroform, ethanol, and methanol. Extraction yield, total phenolic content, flavonoid content, and antioxidant activity were determined, and volatile fingerprints were acquired using the E-nose system. Chemometric analysis was performed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Differences were observed among extraction solvents regarding extraction efficiency, phytochemical composition, and antioxidant activity. PCA successfully discriminated extracts according to solvent polarity, explaining between 70.96% and 90.22% of the total variance depending on the Cistus species analyzed. Furthermore, the PLS-DA model achieved a classification accuracy of 87.5%, with sensitivity and specificity values of 87.5% and 97.5%, respectively. These results demonstrate that the combination of E-nose technology and chemometric tools provides a rapid, non-destructive, and cost-effective approach for the classification and characterization of Cistus extracts according to extraction solvent, highlighting its potential application in quality control and process monitoring of plant-derived products. Full article

Growth is a critical economic trait in all aquaculture industries. To address issues such as germplasm degradation, a comprehensive understanding of the growth and development mechanisms, along with genetic improvement strategies, for Procambarus clarkii (P. clarkii) is urgently required. In this study, we performed whole-genome resequencing on 89 individuals from five cultured stocks to investigate growth traits (body length) and identified a total of 46,919,297 high-quality single nucleotide polymorphisms (SNPs). Based on these SNPs, we conducted principal component analysis (PCA), phylogenetic analysis, and population genetic structure analysis. Furthermore, we performed selective sweep analysis (using FST, Pi, and XP-CLR) and a genome-wide association study (GWAS) to identify genetic variants associated with growth traits. The results revealed significant genetic differentiation among the five cultured stocks, with the Ma’anshan cultured stock exhibiting the fastest linkage disequilibrium (LD) decay. Additionally, long-term aquaculture in different geographical regions resulted in distinct genetic differences among cultured stocks. Through selective sweep analysis, the intersection of FST, Pi, and XP-CLR across the five populations yielded several growth-related candidate genes: Nephrin, Somatostatin, zinc finger protein 154, and yeti. Subsequent the GWAS identified two candidate genes associated with growth traits: Cullin-associated and neddylation-dissociated protein 1 (CAND1) and Baculoviral IAP repeat-containing protein 8 (BIRC8). These genes are presumed to play pivotal roles in the growth and development of P. clarkii. Overall, our findings provide new insights into the genetic mechanisms underlying growth and development in P. clarkii, and these identified genes serve as promising candidates for further functional studies and genetic improvement of this species. Full article

Background: Sexual dimorphism in tooth size has been assessed on radiographs, dental casts, two-dimensional image analysis, and linear measurements are evaluated in two dimensions. These approaches evaluate isolated dimensions and may not adequately represent the three-dimensional complexity of tooth morphology. This study compared conventional odontometric measurements, centroid-based size assessments, and principal component-derived multidimensional size variables to determine whether overall tooth size exhibits sexual dimorphism. Methodology: A total of 120 dental casts (60 males and 60 females) were digitized using a laser surface scanner. Anatomical and geometric landmarks were identified on three-dimensional tooth models using 3D Slicer software. Mesiodistal, buccolingual, and diagonal dimensions were calculated from landmark coordinates, and two- and three-dimensional centroid sizes were derived. Principal component analysis was used to generate integrated multidimensional size variables. Sex differences were evaluated using independent Student’s t-tests, two one-sided tests (TOST) for equivalence, and random-effects meta-analysis. Results: Principal component analysis showed that conventional odontometric measurements and centroid sizes represented a common multidimensional size construct, explaining 79.1–91.3% of the variance across most tooth types. Centroid size demonstrated the highest component loadings, and principal component-derived variables correlated strongly with centroid size (r = 0.945–0.994; p < 0.001). No significant sex differences were observed for any principal component-derived size variable (p > 0.05). Equivalence testing and pooled meta-analysis demonstrated practical and statistical equivalence between males and females across principal component-derived variables, centroid sizes, and conventional odontometric measurements (all TOST p < 0.001). Conclusions: Conventional odontometric measurements, centroid sizes, and principal component-derived variables capture a common multidimensional construct of tooth size. Although isolated dimensions may differ, overall tooth size is practically equivalent between males and females. Full article

Volcanic reservoirs exhibit strong lithological heterogeneity and complex seismic responses, making lithofacies prediction between wells challenging, particularly in the Yingcheng Formation of the Songliao Basin. To address this issue, this study proposes an integrated workflow for volcanic lithofacies prediction based on Principal Component Analysis (PCA)-optimized multi-attribute seismic clustering. Seismic facies are first identified from reflection configuration and external geometry, and three types—chaotic, layered, and shield-like facies—are established and calibrated using well logs and core data. PCA is then applied to reduce attribute redundancy and optimize the attribute set. The selected attributes, including root mean square (RMS) amplitude, energy half-life, and gradient magnitude, are used for multi-attribute clustering. The results indicate that eruption facies dominate the study area and correspond to favorable reservoirs, accounting for the majority of high-quality reservoir zones. In contrast, overflow and volcanic sedimentary facies show comparatively lower reservoir potential. The predicted lithofacies distribution shows strong spatial consistency with well observations, demonstrating the method’s reliability. Overall, the proposed workflow improves lithofacies prediction accuracy and provides an effective tool for reservoir characterization and well deployment in complex volcanic settings. Full article