Search Results (307)

Adult growth hormone deficiency (GHD) is characterized by insulin resistance, visceral adiposity, and adverse body composition. Asprosin (ASP), an adipokine involved in glucose homeostasis and energy metabolism, has emerged as a potential biomarker of metabolic dysfunction. This study investigated longitudinal changes in circulating ASP and their relationships with metabolic, body composition, and bone parameters during 24 months of recombinant human growth hormone (rhGH) therapy. Nineteen adults with severe GHD were prospectively evaluated before and during rhGH replacement. Serum ASP, insulin-like growth factor-1 (IGF-1), metabolic and lipid parameters were assessed, while body composition and bone indices were determined by dual-energy X-ray absorptiometry. Associations between ASP and clinical variables were analyzed using Spearman’s rank correlation. RhGH therapy significantly reduced circulating ASP concentrations at 12 and 24 months, while IGF-1 concentrations increased significantly throughout follow-up. Body fat percentage and fat mass decreased, whereas lean mass increased after 24 months of treatment. Significant changes in bone mineral content and lumbar spine Z-score were also observed after 24 months. ASP was consistently inversely correlated with fasting glucose and developed negative associations with IGF-1 and fat mass during follow-up. Time-dependent associations were also observed with visceral adiposity and lipid parameters, whereas no consistent associations between ASP and skeletal parameters were identified. Long-term rhGH replacement was associated with sustained reductions in circulating ASP concentrations and metabolic remodeling. These findings suggest that ASP may represent a candidate marker of metabolic adaptation during rhGH replacement, although validation in larger prospective cohorts is required. Full article

Mountainous traditional villages may possess valuable inherited landscapes and cultural heritage while differing markedly in their capacity to maintain, interpret, and appropriately use these resources. This mismatch means that a lower overall evaluation score does not necessarily indicate weaker conservation value, and that villages within the same region may require fundamentally different conservation priorities. To explore this issue, this study developed a heritage-oriented resource–service diagnostic framework and applied it to Xiaodianhe, Liyu, and Xuebaizhuang villages in the Southern Taihang Mountains, China. A three-criterion, 15-indicator system combined expert-derived AHP weights with five-grade assessments from 180 respondents, supported by field investigation, UAV imagery, and resource inventories. Traditional heritage resources received the highest criterion weight (0.5490), while the overall scores of Xiaodianhe, Liyu, and Xuebaizhuang were 4.163, 3.751, and 3.168, respectively. More importantly, these scores concealed three distinct conservation situations: Xiaodianhe showed relative resource–service coordination; Liyu retained strong inherited resources but was constrained by targeted deficiencies in interpretation, internal routes, and stay-support conditions; and Xuebaizhuang retained resource potential despite substantial gaps in accessibility, management, and community participation. The comparison demonstrates that differentiated conservation should respond to the internal relationship between inherited resources and service-support conditions rather than to composite rankings alone. Accordingly, conservation actions should be sequenced according to whether a village primarily requires heritage-quality maintenance, targeted service improvement, or basic support-capacity enhancement, while safeguarding authenticity, integrity, landscape continuity, and community continuity. Full article

Traditional goodness-of-fit tests for binary longitudinal data can perform poorly when response-pattern tables are sparse. Concordant information-based tests mitigate this issue by using lower-dimensional marginal information, but their performance under second-order serial dependence has not been systematically examined. Building on this framework, we first discuss a third-order concordant marginal formulation and identify an important limitation in the binary setting: for binary responses, we show that each third-order concordance residual is exactly one half of the sum of the three corresponding pairwise concordance residuals. Thus, the third-order concordance formulation contains no additional information beyond the pairwise concordance residuals, and its rank deficiency in larger binary designs follows from this redundancy. We therefore focus on the second-order concordance statistic and related limited-information diagnostics under AR(2) and MA(2) error structures. Specifically, the AR(2) simulations cover all parameter pairs on the specified grid that satisfy the stationarity conditions, whereas the MA(2) simulations include all 162 grid points satisfying $|{\varphi }_1|+|{\varphi }_2|\le 0.9$ and ${\varphi }_2\ne 0$, which form a symmetric subset of the invertible parameter region. We assess Type I error rates and empirical power across different sample sizes and dependence configurations. The results show that second-order serial dependence, especially negative dependence patterns, can substantially affect test performance. These findings clarify the relative performance of concordance-based diagnostics under the AR(2) and MA(2) parameter settings examined in this study. Full article

Alcohol consumption is associated with increased breast cancer risk, partly due to the accumulation of toxic aldehydes like acetaldehyde, a carcinogenic byproduct of ethanol metabolism. Acetaldehyde Dehydrogenase 2 (ALDH2), a key mitochondrial enzyme, detoxifies acetaldehyde and other reactive aldehydes that drive oxidative stress, DNA damage, and hormonal dysregulation—processes central to carcinogenesis. Although alcohol consumption has been implicated in breast cancer, the role of ALDH2 deficiency itself, in the absence of exogenous alcohol exposure, in mammary gland biology and cancer susceptibility remains unclear. Genetic variants that impair ALDH2 activity are highly prevalent in East Asian populations, where carriers of inactive ALDH2 alleles exhibit impaired aldehyde detoxification. While such individuals are more susceptible to alcohol-related cancers, the effects of ALDH2 deficiency on mammary gland development and homeostasis without alcohol exposure remain unexplored. To investigate the effects of ALDH2 deficiency on mammary proliferation and development, we utilized a C57BL/6-based ALDH2 knockout (Aldh2−/−) mouse model. Our findings revealed that Aldh2−/− mice displayed hyperproliferative mammary glands with increased epithelial cell density, ductal expansion, and increased numbers of Ki67+ cells. Flow cytometry analysis revealed expansion of luminal and basal epithelial subpopulations, accompanied by enhanced mammary epithelial stemness, as indicated by increased mammosphere formation and colony-forming efficiency. At the molecular level, ALDH2 deficiency activated oxidative stress pathways, reflected by elevated 8-OHdG, p38 MAPK, NF-κB, and Nrf2 signaling, along with DNA damage responses involving p53 and H2A.X. We also identified a novel upregulation of RANK and RANKL in Aldh2−/− mammary glands, identifying the RANK/RANKL upregulation associated with NF-κB/p38 MAPK activation and enhanced mammary stemness. Furthermore, hormonal dysregulation was observed, with a significant increase in ERα and PR expression and phosphorylation. Dysregulated ER signaling correlated with enhanced erbB3 activation and downstream signaling, including the cyclin D1–pRb-E2F1 axis. These findings suggest that ALDH2 deficiency, possibly through accumulated endogenous aldehydes, profoundly alters mammary morphogenesis, epithelial repopulation, and stemness. These effects are associated with activation of oxidative stress and DNA damage pathways, together with upregulation of RANKL, estrogen receptor and receptor tyrosine kinase signaling. This study is the first to identify ALDH2 deficiency as a novel factor associated with mammary epithelial alterations that may create a tissue state that could predispose to oncogenic transformation. Full article

Phosphate (P_i) deficiency induces responses that enhance P_i uptake and utilization. Light may influence these responses through photosynthetic carbon supply and signaling. We examined how light affects the early root transcriptional response to P_i deficiency in hydroponically grown soybean. Plants were exposed to phosphate-sufficient (+P) or phosphate-deficient (−P) conditions for 30 h under a light/dark cycle or continuous darkness. Root transcriptomes were analyzed using Oxford Nanopore cDNA sequencing. Principal component analysis (PCA) showed that transcriptomes separated mainly by light, with weaker separation by P_i status. Under light, P_i deficiency induced a broad response involving P_i transport, signaling, transcriptional regulation, lipid remodeling, and metabolism. In darkness, relatively few genes were upregulated under P_i deficiency. This limited response was not caused by a loss of transcriptional responsiveness, because thousands of genes were upregulated by darkness itself. Comparison of fold changes revealed that darkness attenuated, rather than reversed, the −P response. Interaction-ranked gene set enrichment analysis (GSEA) showed that the weaker response in darkness extended across signaling, metabolic, and transport pathways. Light-signaling genes responded strongly to darkness but showed little phosphate-dependent regulation. Root sucrose levels were lower in darkness, consistent with reduced carbon availability as a possible contributor to the attenuated P_i-deficiency response. Full article

Nitrogen deficiency is one of the key factors limiting the growth and raw-material quality of tea plants; however, cell-autonomous mechanisms underlying the response of Camellia sinensis (L.) Kuntze to reduced nitrogen availability remain insufficiently studied. This study characterized biochemical and transcriptomic changes in callus cultures of three tea cultivars (‘Kolkhida’, ‘Karatum’, and cultivar #582) after two months of cultivation on Murashige–Skoog medium lacking nitrogen-containing components. Nitrogen withdrawal caused visible stress symptoms, reduced total nitrogen in ‘Kolkhida’ and ‘Karatum’ (total nitrogen was not determined in cultivar #582), and altered the accumulation of L-theanine, caffeine, and catechins in a cultivar-dependent manner. ‘Kolkhida’ showed decreases in simple catechins, L-theanine, and caffeine; ‘Karatum’ showed increases in simple and gallated catechins together with a decrease in L-theanine; and cultivar #582 showed smaller changes in most measured biochemical traits. RNA-seq identified 1898 differentially expressed genes (DEGs) in ‘Kolkhida’, 4551 in ‘Karatum’, and 9869 in cultivar #582. The enriched Gene Ontology term “response to karrikin” (GO:0080167) was common to all three cultivars, whereas cultivar-specific profiles differed substantially. In the flavonoid biosynthesis pathway, key phenylpropanoid genes were predominantly downregulated in cultivar #582 but induced in ‘Kolkhida’ and ‘Karatum’. RT-qPCR confirmed the direction of change for selected genes. Under the tested in vitro conditions, ‘Kolkhida’ showed the strongest biochemical deterioration, whereas cultivar #582 combined the greatest biochemical stability with the most extensive transcriptomic remodeling. Because total nitrogen was not measured in cultivar #582, its apparent tolerance remains provisional. Tea callus cultures therefore provide a controlled discovery system for cell-autonomous candidate responses, but cultivar rankings and candidate markers require validation in independent callus lines and whole plants. Full article

Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of $3\times {10}^5$ function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy ($\mathrm{RMSE}=5.58\pm 0.64$, $\mathrm{MAE}=3.97\pm 0.46$, $R^2=0.889\pm 0.028$), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. Full article

As a result of population ageing, rehabilitation assistive products for older adults increasingly need to meet long-term user requirements in terms of usability, comfort, and interactive experience. Although existing hand rehabilitation robots (HRRs) are capable of supporting hand training tasks, they still exhibit deficiencies in experience-oriented design, which in turn affect user acceptability and continued use. This study aimed to identify and prioritize user requirements for HRRs and, based on their relative importance, formulate emotional design strategies to inform future UX-oriented development. User interviews, the Kano questionnaire, and an adapted quality function deployment (QFD) requirement-prioritization framework were employed for the systematic investigation. The results revealed a clear priority structure. Personalized and adaptive training (H8) ranked first, followed by function–form integration (H3), continuous functional updates (H15), ergonomic wearing comfort (H4), and voice guidance (H9). Clear and readable screen content (H5), timely feedback (H11), positive emotional motivation (H17), and mobile connectivity (H13) also received relatively high priorities. These findings provide a quantifiable basis for design decision-making and subsequent prototype development. The study identifies user priorities and proposes design strategies; it does not experimentally demonstrate improvements in UX, adherence, or rehabilitation outcomes. Full article

Accurately identifying influential nodes is essential for analyzing network structures and optimizing information propagation. Existing methods predominantly rely on single indicators such as degree, H-index, or k-shell, inherently limiting their ability to capture a node’s true influence. Recent hybrid centrality approaches attempt to address this by combining multiple local and global attributes; however, they typically integrate features through simple weighting or superposition, failing to characterize the intrinsic synergy among structural properties. Furthermore, they often quantify neighbor contributions too coarsely, overlook the regulatory role of edge strength, and some suffer from high computational complexity, limiting scalability. To overcome these deficiencies, we propose WKDH, a novel influential node identification method based on multi-attribute neighbor contributions. WKDH fuses local structural attributes (degree and H-index) with global structural attributes (k-shell) via a multiplicative weighted synergy model, simultaneously capturing local connection “quantity,” local connection “quality,” and global core-layer position. By transforming neighbors’ comprehensive characteristics into regulated contribution degrees, WKDH mitigates excessive self-attribute interference and accurately reflects the actual propagation potential of edges. Notably, the method achieves linear computational complexity of $O\left(m\right)$. Experimental results on nine real-world and six artificial networks demonstrate that WKDH outperforms nine established indicators in terms of node influence ranking, identification of high-influence nodes, and measuring propagation capability. Moreover, WKDH exhibits strong universality across diverse network structures, as it operates without parameter tuning. Full article

Background/Objectives: Oxidative stress is a major cause of RBC membrane damage, especially in individuals with G6PD deficiency who have impaired antioxidant defenses. Triphala, a phenolic-rich herbal formulation with known antioxidant activity, was evaluated for its phytochemical profile, antioxidant and anti-hemolytic effects, and molecular mechanisms in H₂O₂-induced oxidative stress models using normal and G6PD-deficient human RBCs. Methods: Triphala aqueous extract was characterized using LC-MS and GC-MS. Antioxidant activity was evaluated by DPPH and ABTS assays. Cytotoxicity, membrane stability, and protection against H₂O₂-induced hemolysis were assessed in normal and G6PD-deficient RBCs. Network pharmacology, molecular docking and MD simulation analyses were performed to predict antioxidant-related mechanisms. Statistical analysis was conducted using one-way ANOVA (p < 0.05). Results: LC-MS identified gallic acid as the predominant phenolic compound, while GC-MS revealed pyrogallol as the major constituent. The extract showed strong radical scavenging activity and significantly reduced H₂O₂-induced hemolysis in both normal and G6PD-deficient RBCs without cytotoxicity (p < 0.05). Network pharmacology revealed that G6PD-related antioxidant regulation, oxidative stress response, and inflammatory signaling pathways are the key enriched biological processes. Network pharmacology analysis ranked PPARG, PTGS2, EGFR, MMP9, TLR4, ACE, REN, PPARA, SERPINE1, and MMP2 as the top hub proteins, highlighting their central roles in oxidative stress, inflammation, and metabolic signaling pathways. Kynurenic acid binds strongly to PTGS2 (COX-2) and ACE with binding affinities below −7.0 kcal/mol, forming multiple hydrogen bonds that stabilize its interactions within the active sites. MD simulations confirmed that kynurenic acid binds stably to ACE and PTGS2, with RMSD values plateauing near 2.4 Å and 3.0 Å, RMSF values mostly below 2 Å, and recurrent hydrogen bonding and electrostatic contacts with key residues, collectively underscoring its conformational stability, adaptive flexibility, and modulatory potential. Conclusions: Triphala aqueous extract exhibits potent antioxidant and anti-hemolytic activities and may serve as a natural adjunct strategy for reducing oxidative damage in G6PD deficiency and related RBC disorders. Full article

Construction delays remain a persistent challenge in Australian construction projects, contributing to cost escalation, disrupted work sequences, contractual claims, and reduced confidence in project delivery. Although delay causes have been widely investigated, existing studies often provide broad factor lists and prioritise risks using single-dimension or inconsistent scoring approaches. This limits guidance on which delay risks should receive priority attention when project teams face constrained time, cost, and management resources. This study addresses this limitation by quantifying and prioritising 22 validated delay risk factors in Australian construction projects. Probability of occurrence and schedule impact were evaluated as separate judgement dimensions before being integrated into an overall measure of risk criticality. Data were collected from 48 experienced Australian construction professionals. A dual-dimension Fuzzy Best–Worst Method was applied to derive separate ratio-scale weights for probability of occurrence and schedule impact, with dimension-specific consistency screening used to improve judgement reliability. The resulting weights were integrated using a probability–impact formulation and mapped onto a 5 × 5 Probability–Impact Matrix through quantile-based discretisation. A 10,000-iteration Monte Carlo robustness analysis was subsequently conducted to assess the stability of the resulting rankings under alternative expert-selection and weighting scenarios. The results indicate that the delay risks perceived by the participating professionals as having the highest combined probability and schedule impact are predominantly governance-, approval-, and coordination-related, particularly owner late decisions, change-approval delays, owner requirement changes, cost-estimation deficiencies, design-approval delays, and inadequate planning. The Monte Carlo analysis further indicated that the principal risk rankings remained relatively stable under variations in expert aggregation. Overall, the integrated FBWM–PIM framework provides a structured and practically interpretable approach for eliciting and prioritising expert perceptions of construction delay risk and translating them into an actionable classification tool for allocating limited risk management resources. Full article

China’s pork sector is a major component of the national food system. Establishing plant-based pork supply chains requires coordination across production, quality control, infrastructure, logistics, information exchange, and market formation. However, previous studies generally examine these barriers separately, limiting understanding of how they interact within the wider food-supply system. This study identifies and analyzes the systemic barriers to establishing plant-based pork supply chains in China. An integrated Fuzzy Delphi Method (FDM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is applied. FDM is used to refine and validate 14 contextually relevant barriers based on expert consensus, while DEMATEL examines their direct and indirect relationships, systemic prominence, and net causal influence. Insufficient research and development funding and deficiencies in quality control emerge as the strongest net causal barriers. High infrastructure investment also belongs to the cause group, while technological, operational, and market-related barriers occupy different causal and dependent positions within the wider system. The results support a sequenced intervention strategy that begins with innovation capacity, quality assurance, and infrastructure, followed by operational coordination and market formation. This study contributes by moving beyond barrier identification and ranking to explain how multiple barrier domains interact and how interventions can be prioritized. The analysis concerns supply chain establishment and does not directly assess the environmental, economic, or social sustainability performance of plant-based pork. Full article

Unmanned surface vehicle (USV) formations operating under GNSS denial require accurate relative localization using proprioceptive sensors and inter-vehicle ranging. This paper presents an anomaly-aware adaptive extended Kalman filter for four-USV formations using inertial measurements, compass, and ultra-wideband ranging, and systematically characterizes its operating envelope. Observability analysis establishes that S-curve maneuvering achieves structural rank 24, with only global translation unobservable, while straight-line motion leads to a rank deficiency of exactly seven dimensions All four gyroscope biases remain observable under both trajectories. The proposed filter integrates chi-square testing, cumulative sum (CUSUM) detection, and bias drift rate monitoring to trigger coordinated R adaptation and Q-boost mechanisms. Controlled experiments spanning outlier magnitudes and drift rates reveal three performance regimes, clean conditions with equivalent performance across all variants, moderate outliers $[3{\sigma }_d,10{\sigma }_d]$ where the proposed method achieves 4.8–13.4% improvement, and extreme outliers where all robust methods converge. Critically, pure bias drift experiments expose a structural limitation of single-hypothesis, residual domain robustification within the tested drift range—all variants exhibit equivalent performance across the tested drift rates, analytically attributable to Kalman gain partitioning that distributes innovations between position and bias subspaces. The characterized operating envelope establishes that robust mechanisms provide measurable benefits for transient anomalies but encounter hard boundaries under persistent drift conditions, with all variants converging to equivalent performance across the tested range, necessitating multi-hypothesis or constraint-based approaches. Full article

Background: This study aimed to compare a chemiluminescence immunoassay (CLIA; Siemens Atellica IM 1600) with an automated liquid chromatography–tandem mass spectrometry (LC–MS/MS) analyser (Thermo Scientific Cascadion SM) for serum 25-hydroxyvitamin D [25(OH)D] measurement in unselected outpatients, and its impact on clinical classification. Methods: 25(OH)D was measured on 1064 paired serum samples (828 women, 236 men; age 3–111 years); both platforms participated in the Vitamin D Standardisation Program (VDSP). Agreement was assessed by the Wilcoxon signed-rank test, Passing–Bablok regression, Bland–Altman analysis on log-transformed values, Breusch–Pagan testing for heteroscedasticity, consistency-based intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient, and Cohen’s kappa with McNemar’s test on six- and two-category clinical classifications. Results: Methods differed significantly ($p<2.2\times {10}^{-16}$). Passing–Bablok regression showed proportional bias (slope 1.33; 95% confidence interval [CI], 1.28–1.38; LC–MS/MS higher). Consistency ICC was 0.928 (95% CI, 0.919–0.936). Log-Bland–Altman bias was 35.5% (limits of agreement, $-18.3$% to $+124.5$%; heteroscedasticity, $p=5.18\times {10}^{-11}$). Cohen’s kappa was 0.32 unweighted and 0.72 weighted on six clinical categories, and 0.56 on a two-class scheme (McNemar $p<2.2\times {10}^{-16}$). In discordant pairs, CLIA classified deficiency 118-fold more often than LC–MS/MS. Conclusions: In our study, despite preserved rank ordering, a non-linear concentration-dependent bias and different limits of agreement caused systematic differences in the classification of vitamin D deficiency. Adoption of automated LC–MS/MS can substantially reclassify outpatients toward sufficiency, with implications for supplementation and laboratory harmonisation. Full article

Direction-of-arrival (DOA) estimation with coprime arrays can synthesize an enlarged virtual aperture from a limited number of physical sensors. However, coherent incident sources lead to rank deficiency of the source covariance matrix, while unknown nonuniform sensor noise mainly contaminates the zero-lag component of the difference-coarray covariance. These two effects jointly degrade conventional Coarray Root-MUSIC, Coarray ESPRIT, and interpolation-based virtual-array methods. To address this problem, this paper proposes a Toeplitz–Hankel structured covariance reconstruction method for coherent-source DOA estimation with coprime arrays under unknown nonuniform noise. The method first performs redundancy-aware difference-coarray lag averaging. The zero-lag component is then suppressed during missing-lag interpolation to reduce the bias caused by sensor-dependent noise powers. A Toeplitz positive semidefinite projection is used to enforce covariance validity, and a relaxed Hankel truncated-singular-value-decomposition refinement is introduced to enhance the low-rank spectral structure of the reconstructed virtual covariance sequence. Finally, multi-scale forward–backward spatial smoothing MUSIC is applied for coherent-source DOA estimation. Simulation results with a coprime array of $M=4$ and $N=5$ show that the proposed method provides more accurate and stable DOA estimates than Coarray Root-MUSIC, Coarray ESPRIT, and RV-TSI. Compared with CVX-based THSCR, the proposed method avoids semidefinite programming and nuclear-norm optimization and reduces the average runtime from approximately $11.2$ s per trial to approximately $0.11$ s per trial under the tested setting. Full article