Search Results (253)

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Introduction: Nerve-sparing (NS) during robot-assisted radical prostatectomy (RARP) plays a critical role in postoperative functional recovery, particularly urinary continence and erectile function. Despite the importance of precise neurovascular bundle (NVB) preservation, intraoperative assessment of NS quality remains largely subjective and lacks standardized evaluation tools. The aim of this study was to develop and preliminarily evaluate a structured intraoperative scoring system designed specifically for assessing NS quality during RARP. Methods: A novel 10-point intraoperative NS scoring system (NSQ Score) based on five domains was developed: dissection plane, bleeding control, bundle manipulation, continuity of dissection, and symmetry. Each parameter was rated on a 0–2 scale. Thirty robot-assisted radical prostatectomy (RARP) procedures performed in 2024 were randomly selected from a prospectively maintained institutional surgical video archive. Cases were not pre-filtered based on tumor stage, surgical difficulty, or intraoperative complexity. High-definition video recordings of the nerve-sparing phase were anonymized and independently evaluated by three experienced observers blinded to patient outcomes and to each other’s assessments. Inter-rater agreement was analyzed using weighted Cohen’s kappa statistics with quadratic weights, complemented by exact and near-agreement proportions. Cluster bootstrap resampling was applied to account for bilateral observations. Results: A total of 48 evaluable observations were analyzed. The overall inter-rater agreement demonstrated a weighted kappa of 0.41 (95% CI 0.36–0.48), indicating fair-to-moderate agreement among reviewers. Exact agreement occurred in 43% of observations, while near-agreement (allowing one ordinal level difference) reached 98%. Among individual parameters, symmetry demonstrated the highest reliability with substantial agreement (κ = 0.70; 95% CI 0.58–0.81). Other domains showed fair agreement, including intraoperative bleeding (κ = 0.36), continuity of dissection (κ = 0.39), bundle manipulation (κ = 0.34), and dissection plane (κ = 0.27). Agreement levels were comparable between left- and right-sided dissections. Conclusions: We propose a novel structured intraoperative scoring system for evaluating nerve-sparing quality during RARP. The scale is simple, procedure-specific, and feasible for structured postoperative or video-based assessment. Preliminary results demonstrate fair-to-moderate inter-rater reliability with very high near-agreement, supporting the feasibility of this tool for clinical use. The proposed scoring system may facilitate standardized training, objective performance assessment, and future studies correlating intraoperative NS quality with functional outcomes. Full article

Background: Orthodontic treatment is increasingly recognised as a complex, patient-centred intervention whose impact extends beyond occlusal correction to include physical comfort, psychosocial well-being, and self-perceived esthetics. Oral health-related quality of life (OHRQoL) has therefore become a key outcome for evaluating orthodontic care across all treatment stages. Aim: This narrative review of 140 studies synthesises current evidence on OHRQoL changes in orthodontic patients before treatment, during active therapy, and after treatment completion, with particular emphasis on temporal patterns and appliance-related differences. Methods: A comprehensive narrative review of 140 studies was conducted using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar (search period: inception to December 2025). Studies assessing OHRQoL or patient-reported outcomes in orthodontic patients of any age were included. Only studies employing validated instruments, such as OHIP, CPQ, OIDP, and PIDAQ, were considered. Dual-reviewer agreement was assessed using Cohen’s kappa (κ = 0.82). Formal risk-of-bias assessment was conducted using ROBINS-I for non-randomised studies and the Cochrane Risk of Bias tool for RCTs. Sensitivity analyses were performed comparing high-quality studies (low risk of bias, n = 52) versus all included studies. Results: The reviewed evidence consistently demonstrates that malocclusion is associated with impaired baseline OHRQoL, particularly affecting psychosocial and esthetic domains. The early phase of orthodontic treatment is marked by a transient deterioration in OHRQoL due to pain, discomfort, speech disturbances, and functional limitations (87% of studies report pain peaks within 24–48 h; 79% report resolution by 4–7 days). These effects typically diminish as patients adapt to the appliance. Progressive improvement is observed during mid-treatment, while treatment completion is associated with substantial long-term gains in self-esteem, social functioning, and overall quality of life. Appliance type influences short-term outcomes, with clear aligners generally associated with better early OHRQoL than fixed and lingual systems (65–75% of studies favour aligners for early comfort; 78% favour lingual systems for esthetic satisfaction). Conclusions: Orthodontic treatment follows a dynamic, time-dependent OHRQoL trajectory characterised by short-term impairment and significant long-term psychosocial benefits. Systematic integration of validated OHRQoL measures into orthodontic care may enhance patient-centred decision-making and optimise clinical outcomes. Full article

Background: Gadoxetic acid-enhanced MRI is essential for detecting and characterizing focal liver lesions. However, transient severe motion artifacts in the arterial phase can degrade image quality. Gadoxetic acid dilution has been proposed to mitigate these artifacts, but its impact on multiple arterial phase acquisition remains unclear. Objective: To evaluate the effect of gadoxetic acid dilution on image quality across multiple arterial phases in liver MRI, incorporating a phase-by-phase analysis. Methods: This retrospective study included 81 patients (52 men, 29 women; mean age 70.1 years) who underwent serial gadoxetic acid-enhanced MRI with undiluted and diluted contrast (1:1 saline dilution). MRI was performed on 1.5 T and 3.0 T scanners with a standardized injection rate of 1.0 mL/s. Two radiologists independently rated anatomic conspicuity, respiratory motion artifacts, and overall image quality using a Likert scale (1 to 5 with higher scores indicating better quality). A phase-by-phase analysis was conducted after a three-month washout period. Wilcoxon signed-rank tests were used for statistical comparisons, and inter-rater agreement was assessed with quadratic kappa coefficients. Results: Inter-observer agreement was substantial (ƙ = 0.602–0.702). Phase-by-phase analysis revealed significant improvement in image quality for the first three arterial phases (p = 0.003, 0.005, 0.050). Although the diluted method showed higher scores, the differences were not statistically significant in anatomic conspicuity (3.73 vs. 3.59, p = 0.110), respiratory artifacts (3.54 vs. 3.41, p = 0.291), and overall image quality (3.67 vs. 3.51, p = 0.083). Conclusions: Gadoxetic acid dilution improves image quality in early arterial phases of liver MRI, suggesting its potential to reduce motion artifacts. Full article

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities. Full article

Autophagy is a critical cellular mechanism that regulates the degradation of misfolded and aggregated proteins and non-functional intracellular organelles. Based on the fundamental qualities of the substrates targeted for degradation and the distinct molecular mechanisms involved, autophagy can be classified into three major types: macroautophagy, microautophagy, and chaperone-mediated autophagy (CMA). Sequestosome 1 (SQSTM1)/p62, which functions as a signaling hub integrating nuclear factor kappa B (NF-κB), the mechanistic target of rapamycin complex 1 (mTORC1), and Kelch-like ECH-associated protein 1 (Keap1)–nuclear factor erythroid 2–related factor 2 (NRF2) pathways, serves as a selective macroautophagy/autophagy receptor that binds ubiquitinated cargo proteins and recruits them to the autophagosome for subsequent degradation in the autolysosome. Furthermore, the phase separation of p62 is an important regulatory process in the autophagy mechanism, but recent studies have demonstrated that impaired or excessive autophagy mediated by p62 is associated with cancer development. This review summarizes the role of autophagy—including its types, mechanisms, and the pathway related to the ubiquitin-dependent selective autophagy receptor p62—in cancer progression. Full article

In recent years, insect-derived peptides have attracted attention for their potential biological activities, particularly antioxidant properties. This study assessed the antioxidant activity of two widely consumed edible insects, T. molitor and A. diaperinus larvae, using cell-free and cell-based approaches. Whole lyophilized larvae, digestion products from the oral, gastric, and intestinal phases, as well as the <3 kDa permeate fraction (D-P3) derived from the intestinal digestion phase, were evaluated using biochemical antioxidant assays. Overall, digested samples exhibited higher antioxidant capacity than their undigested counterparts. At the cellular level, treatment of LPS-stimulated, PMA-differentiated THP-1 macrophages with A. diaperinus D-P3 was associated with increased mRNA expression of genes related to antioxidant defense, including NFE2-like bZIP transcription factor 2 (NFE2L2, also known as Nrf2), glutathione-disulfide reductase (GSR), superoxide dismutase 1 (SOD1), and catalase (CAT), whereas T. molitor D-P3 preferentially modulated nuclear factor kappa B p50 subunit (NFKB1) and nuclear factor kappa B p65 subunit (RELA). Overall, these findings indicate that gastrointestinal digestion enhances the bioaccessibility of antioxidant components in both edible insect species while revealing species-specific transcriptional responses under in vitro inflammatory conditions. This multilevel assessment provides mechanistic insight into the antioxidant-related biological activity of digestion-derived insect peptides and supports their further investigation as functional ingredients in food and feed systems. Full article

Background: Malnutrition in adolescents remains a significant public health issue worldwide, with undernutrition and overweight often coexisting. Accurate nutritional screening during adolescence is complicated by variability in biological maturation and class imbalance, particularly among underweight adolescents. Objective: This study aims to develop and validate machine learning models for classifying the nutritional status of adolescents, accounting for class imbalance and biological maturation, and to evaluate model stability and variable importance at different stages of peak height velocity (PHV). Methods: In this cross-sectional study, 4232 adolescents aged 11 to 18 years were recruited from nine educational institutions in Tunisia. Their nutritional status was classified according to the International Obesity Task Force (IOTF) BMI thresholds into three categories: underweight (14.4%), normal weight (68.3%), and overweight (17.2%). Ten anthropometric, behavioral, and maturation-related predictors were analyzed. Six supervised machine learning algorithms were evaluated using a 70/30 stratified split between training and test sets, with five-fold cross-validation. Class imbalance was addressed by ROSE combined with cost-sensitive learning. Model performance was assessed using accuracy, Cohen’s kappa coefficient, macro F1 score, sensitivity, specificity, and AUC. Results: The cost-sensitive Random Forest (RF) model achieved the best overall performance, with an accuracy of 0.830, a macro F1 score of 0.767, a macro-AUC of 0.921, and a macro- sensitivity of 0.743. The class-specific sensitivities were 0.70 (underweight), 0.91 (normal weight), and 0.62 (overweight), with no major misclassification between the extreme categories. Performance remained stable across the different maturation phases (accuracy from 0.823 to 0.839), with optimal discrimination in the pre-PHV (macro-AUC = 0.936; sensitivity for underweight = 0.82) and post-PHV (macro-AUC = 0.931) periods. Body mass was the main predictor (importance = 1.00), followed by waist circumference (0.34–0.53). The importance of age for classifying underweight increased significantly from the pre-PHV (0.10) to the post-PHV (0.75) period. A two-stage hierarchical model further improved underweight detection (stage 1 AUC = 0.911; sensitivity = 0.732). Conclusions: A cost-sensitive RF model, combined with ROSE, provides robust classification of adolescents’ nutritional status maturation, significantly improving underweight detection while preserving overall accuracy. This approach is particularly well-suited to public health screening in schools as a first-stage assessment that requires clinical confirmation and promotes a maturation-aware interpretation of nutritional risk among adolescents. Full article

Cropland dynamics in ecologically fragile regions are central to balancing food security and ecological integrity in the Yellow River Basin. Ningxia Hui Autonomous Region is used as a case study. An integrated simulation framework is developed by coupling an improved grey prediction model (Improved GM(1,1)) with the CLUMondo spatial model. The analysis addresses four questions: how cropland changed during 2009–2024, which drivers explain cropland suitability and transitions, what spatial resolution is appropriate for implementation, and how cropland patterns differ under alternative development pathways for 2025–2040. Historical cropland change in Ningxia during 2009–2024 is quantified, and spatial patterns for 2025–2040 are projected under three scenarios: business-as-usual (BAU), ecological protection (EP), and rapid urbanization (URE). Cropland change during 2009–2024 shows pronounced phased fluctuations and a stable redistribution pattern described as “southern reduction and northern replenishment, urban decrease and rural increase”. Population growth, economic expansion, and policy regulation jointly drive this spatiotemporal reconfiguration. Land demand forecasting is improved by introducing a metabolism mechanism and residual correction into the grey model, which reduces mid- to long-term divergence. Multi-scale logistic regression tests show the highest AUC at 50 m, with AUC values exceeding 0.8 across land categories, and this resolution is used for model implementation. Model performance is evaluated using AUC, Kappa, and overall accuracy, supporting the applicability of the framework in arid, ecologically fragile regions. Scenario simulations reveal clear divergence in future spatial outcomes. BAU maintains sustained pressure on cropland protection and ecological security. URE increases the risk of encroachment on high-quality cropland in the central–northern irrigated areas due to urban expansion. EP constrains construction land growth and secures strategic ecological spaces, thereby slowing the loss of high-quality cropland while maintaining development capacity. These results provide a transparent basis for scenario-based territorial spatial planning in Ningxia and offer transferable evidence for managing cropland–ecology tradeoffs in arid and semi-arid regions. Full article

High-spatial-resolution remote sensing imagery provides a data foundation for fine-grained land use classification. However, due to long revisit cycles and susceptibility to cloud cover, large-area imagery often suffers from temporal inconsistency, which severely limits the classification accuracy of traditional unified models. To address this issue, this study proposes a geographic entity-oriented, spatiotemporally coupled land use classification method for high-resolution remote sensing imagery, with agricultural land (including paddy fields, dry farmland and gardens) as an example for validation. In this method, the study area is first divided into multiple sub-regions based on image acquisition time, ensuring temporal consistency within each sub-region. A dedicated deep texture feature extraction model is then constructed for each sub-region. This model is adapted from the advanced CAPTN texture recognition network: its classification head is removed, and a multi-scale feature fusion module is introduced, transforming it into an encoder focused on extracting spatial texture feature maps. Additionally, a self-supervised loss function combining masked feature reconstruction and cross-view consistency is designed to improve the quality of the learned texture features. During the prediction stage, the corresponding feature extractor is invoked based on the temporal phase of the imagery to generate a full-region texture feature map. This feature map is then cropped using land parcel vectors, and statistical feature vectors describing the texture attributes of each parcel are formed by calculating the mean and standard deviation of the features within each parcel. Finally, a Random Forest classifier is employed to determine the land parcel categories. This study uses the Jiangjin District of Chongqing City as the experimental area. The results show that, compared to training a unified deep learning model directly on full-region multi-temporal imagery or using traditional texture features, the proposed spatiotemporally coupled classification framework achieves significant improvements in overall accuracy and Kappa coefficient, reaching 92.3% and 0.89, respectively. Full article

Heat stress poses a considerable challenge to the modern poultry industry by negatively impacting immune system maturation and eliciting inflammatory responses. Peroxisome proliferators-activated receptors α (PPARα), predominantly expressed in metabolically active tissues such as skeletal muscle, are essential for regulating the inflammatory process. Moreover, our recent research has found that heat stress down-regulates the transcription of PPARα in broiler chickens. To study if PPARα regulation is involved in heat-stress-induced skeletal muscle inflammatory response in broiler chickens, 180 male Arbor Acres (AA) broilers aged 22 days were randomly assigned to three experimental groups: a thermoneutral condition group at 21 °C, a heat stress group at 31 °C and a heat stress group at 31 °C supplemented with the PPARα activator fenofibrate. After 7 days of adaptive feeding, the broilers were subjected to a 14-day formal experimental phase. Results demonstrated that heat stress decreased the spleen and thymus index and increased serum and breast muscle inflammatory factor concentrations (p < 0.05). Moreover, heat-stress-induced abnormal breast muscle fiber morphology in broiler chickens. Furthermore, heat stress significantly up-regulated nuclear factor kappa-B (NF-κB) expression in boiler chickens (p < 0.05). However, activating PPARα through fenofibrate improved the growth performance (p < 0.05), enhanced immune organ indexes (p < 0.05), reduced inflammatory factor concentrations (p < 0.05), alleviated breast muscle fiber morphology damage and suppressed NF-κB expression (p < 0.05) in the breast muscle of broiler chickens. Based on our previous research, these results collectively underscore that heat stress induced inflammation and up-regulated NF-κB in the breast muscle of broiler chickens by inhibiting PPARα. Full article

Monitoring spatiotemporal dynamics of aboveground carbon (AGC) storage at the tree species level is crucial for evaluating the ecological impacts of large-scale infrastructure projects and facilitating accurate ecological environmental management. However, existing studies heavily rely on interannual coarse-spatial-resolution forest-type products, leading to significant uncertainties in carbon estimation, particularly in fragmented linear engineering zones. This study integrated Sentinel-1/2 data with a random forest (RF) model to map tree species distribution (overall accuracy = 85.18%; Kappa = 0.8319) and AGC estimation (R² = 0.7057; RMSE = 13.35 Mg ha⁻¹) at a 10 m resolution in the Pinglu Canal Basin from 2019 to 2024. The results revealed a total AGC decline of 16.88% across the watershed. Spatially, the Environmental Impact Area (EIA) functioned as the primary disturbance core (experiencing a 28.91% loss), while the Ecological Buffer Area (EBA) acted as a regional carbon stabilizer. At the species level, while Eucalyptus grandis accounted for the majority of carbon depletion, Pinus massoniana exhibited a resilience-driven rebound in the mid-construction phase. Meanwhile, Litchi chinensis and other native species demonstrated steady gains. Cumulatively, these species-specific carbon gains associated with natural growth and restoration initiatives effectively offset 34.45% of the carbon loss. These findings provide quantitative evidence supporting the potential of green engineering to mitigate the ecological footprint of infrastructure development. This study offers a robust monitoring tool for low-carbon infrastructure and directly supports the United Nations Sustainable Development Goal 15 (SDG 15) related to forest conservation and ecological restoration. Full article

The Chongming Dongtan wetland, a representative coastal wetland in East Asia, faces a significant ecological threat from the invasive species Spartina alterniflora. The mixed ecotone formed between this invasive species and the native Phragmites australis serves as a highly sensitive and critical indicator of alterations in wetland ecosystem structure and function. Using spring and autumn Sentinel-2 imagery from 2016 to 2023, this study developed an integrated method that combines a three-dimensional feature space with multi-threshold Otsu segmentation to accurately extract the mixed S. alterniflora–P. australis ecotone. The spatiotemporal dynamics of the mixed ecotone were analyzed at multiple temporal scales using a centroid migration model and a newly defined Seasonal Area Ratio (SAR) index. The results suggest that: (1) Near-infrared reflectance and NDVI were identified as the optimal spectral indices for spring and autumn, respectively. This approach led to a classification achieving an overall accuracy of 87.3 ± 1.4% and a Kappa coefficient of 0.84 ± 0.02. Notably, the mixed ecotone was mapped with producers’ and users’ accuracies of 85.2% and 83.6%. (2) The vegetation followed a distinct land-to-sea ecological sequence of “pure P. australis–mixed ecotone–pure S. alterniflora”, predominantly distributed as an east–west trending belt. This pattern was fragmented by tidal creeks and micro-topography in the northwest, contrasting with geometrically regular linear features in the central area, indicative of human engineering. (3) The ecotone showed continuous seaward expansion from 2016 to 2023. Spring exhibited a consistent annual area growth of 13.93% and a stable seaward centroid migration, whereas autumn exhibited significant intra-annual fluctuations in both area and centroid, likely influenced by extreme climate events. (4) Analysis using the Seasonal Area Ratio (SAR) index, defined as the ratio of autumn to spring ecotone area, revealed a clear transition in the seasonal competition pattern in 2017, initiating a seven-year spring-dominant phase after a single year of autumn dominance. This spring-dominated era exhibited a distinctive sawtooth fluctuation pattern, indicative of competitive dynamics arising from the phenological advancement of P. australis combined with the niche penetration of S. alterniflora. This study elucidates the multiscale competition mechanisms between S. alterniflora and P. australis, thereby providing a scientific basis for effective invasive species control and ecological restoration in coastal wetlands. Full article

Berberine (BBR), a protoberberine alkaloid with a long history of medicinal use, has consistently demonstrated benefits in glucose–lipid metabolism and inflammatory balance across both preclinical and human studies. These diverse effects are not mediated by a single molecular target but by BBR’s capacity to restore network coordination among metabolic, immune, and microbial systems. At the core of this regulation is an AMP-activated Protein Kinase (AMPK)-centered mechanistic hub, integrating signals from insulin and nutrient sensing, Sirtuin 1/3 (SIRT1/3)-mediated mitochondrial adaptation, and inflammatory pathways such as nuclear Factor Kappa-light-chain-enhancer of Activated B cells (NF-κB) and NOD-, LRR- and Pyrin Domain-containing Protein 3 (NLRP3). This hub is dynamically regulated by system-level inputs from the gut, mitochondria, and epigenome, which in turn strengthen intestinal barrier function, reshape microbial and bile-acid metabolites, improve redox balance, and potentially reverse the epigenetic imprint of metabolic stress. These interactions propagate through multi-organ axes, linking the gut, liver, adipose, and vascular systems, thus aligning local metabolic adjustments with systemic homeostasis. Within this framework, BBR functions as a negentropic modulator, reducing metabolic entropy by fostering a coordinated balance among these interconnected systems, thereby restoring physiological order. Combination strategies, such as pairing BBR with metformin, Sodium-Glucose Cotransporter 2 (SGLT2) inhibitors, and agents targeting the microbiome or inflammation, have shown enhanced efficacy and substantial translational potential. Berberine ursodeoxycholate (HTD1801), an ionic-salt derivative of BBR currently in Phase III trials and directly compared with dapagliflozin, exemplifies the therapeutic promise of such approaches. Within the hub–axis paradigm, BBR emerges as a systems-level modulator that recouples energy, immune, and microbial circuits to drive multi-organ remodeling. Full article