Search Results (2,899)

Objective: To characterize the structural organization of Enhanced Recovery After Surgery (ERAS) component implementation in gynecologic oncology and determine whether ERAS elements operate as an interconnected perioperative system during early pathway integration. Methods: This study represents a secondary analysis of the prospective multicenter Enhanced Recovery in Gynecologic Oncology (ERGO) cohort, including the first 300 consecutive patients undergoing surgery for gynecologic malignancy across five tertiary institutions. Components with prevalence between 5% and 95% were included in a regularized Ising network model to estimate conditional dependencies between pathway elements. Node-level centrality metrics and global network characteristics were calculated to identify structurally influential ERAS components and to describe the overall implementation architecture. Results: Thirteen central ERAS components met the predefined prevalence criterion (5–95%) and were included in the conditional dependency network. The estimated network demonstrated substantial inter-component connectivity, indicating that ERAS practices were frequently implemented in coordinated patterns rather than as isolated interventions. Centrality analysis identified postoperative laxatives or chewing gum, tranexamic acid administration, perioperative intravenous fluid management, and avoidance of drain placement as highly connected elements within the network. Early nutritional advancement and postoperative bowel stimulation measures also demonstrated relatively central positions within the recovery-related component cluster. Community detection analysis revealed distinct modules of co-adopted ERAS practices spanning multiple perioperative phases. Conclusions: ERAS implementation in gynecologic oncology appears to follow a structured architecture characterized by interconnected perioperative practices rather than independent protocol elements. Understanding these implementation structures may help guide targeted quality-improvement strategies aimed at optimizing ERAS integration in routine clinical practice. Full article

This study investigates the decay-like degradation mechanisms of the matrix material in composite insulators, focusing on the pronounced influence of humid environments on partial discharge (PD) characteristics and degradation pathways. A sealed chamber discharge platform was established, integrating PD signal monitoring, surface characterization, and gas chromatography-mass spectrometry (GC-MS) with molecular network analysis to examine the synergistic effects of thermal influences from PD and active atmospheric particles at humidity levels of 0% RH, 50% RH, and 100% RH. Results show that dry conditions favor high-energy, low-repetition-rate discharges, promoting cleavage and recombination of high-bond-energy bonds (e.g., benzene rings and (α)C–O), yielding primarily long-chain carboxylic acids (C9 and above). In contrast, humid conditions shift to low-energy, high-repetition-rate discharges, with water vapor decomposition generating highly oxidizing hydroxyl radicals (·OH). These facilitate selective scission of lower-bond-energy (β)C–O bonds and deep oxidation, significantly increasing short-chain dicarboxylic acids—especially oxalic acid—whose acidity and water solubility are nearly an order of magnitude higher than in dry environments, becoming the dominant acidic products. The work demonstrates that many PD-generated organic acids act as intrinsic corrosive agents in insulating systems, independent of ambient nitric acid. This elucidates, at the reaction pathway level, how high humidity modulates PD to enhance corrosive acid production, providing a microchemical basis for understanding regional decay-like failure patterns in composite insulators. Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article

Plants must continuously balance the trade-offs between growth and defense, a constraint that is exacerbated by biotic and abiotic stresses, particularly when they occur together. Ethylene (ET) serves as a central, integrative regulatory node controlling this by linking developmental programs to stress-responsive signaling networks. Advances at the molecular and systems levels have revealed that ET mediates the redistribution of metabolic resources via coordinated regulation of its synthesis, perception, and downstream signaling. The ETR (Ethylene Receptor)-CTR1 (Constitutive Triple Response 1)-EIN2 (Ethylene Insensitive 2)-EIN3(Ethylene Insensitive 3) signaling module lies at the core of this network, integrating multiple hormonal pathways. Through dynamic crosstalk with jasmonic acid (JA), salicylic acid (SA), abscisic acid (ABA), auxin (AUX), and gibberellins (GA), ET enables the fine-tuned coordination of growth inhibition, immune activation, and stress acclimation in response to environmental fluctuations. Processes such as induced systemic resistance, programmed cell death, and architectural plasticity further reinforce this regulatory framework, with ethylene-responsive transcription factors, including ERFs (ethylene responsive factor gene family) and WRKYs, acting as critical convergence points. Emerging insights into ACC (1-aminocyclopropane-1-carboxylic acid) -dependent signaling, chromatin remodeling, and tissue-specific regulation expand the functional scope of ET beyond traditional hormone paradigms. At the same time, the ability of pathogens to manipulate ET signaling underscores its dual role in both promoting immunity and facilitating susceptibility. By integrating molecular, physiological, and ecological perspectives, this review highlights ET as a central coordinator of plant stress resilience and growth optimization, providing a unifying framework for understanding how plants adapt to complex and dynamic environments. Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

Chemical hybridization agents (CHAs) enable efficient, large-scale hybrid seed production, yet their mechanisms remain poorly understood. Understanding how CHAs induce male sterility at the metabolic level is important for both basic pollen biology and crop breeding. Here, we performed integrated metabolomic analyses to investigate the metabolic basis of the action of trifluoromethanesulfonamide (TFMSA) across multiple species and tissues. TFMSA treatment induced systemic metabolic reprogramming across species, prominently affecting amino acid metabolism, central carbon metabolism, and one-carbon metabolism. Although individual metabolite responses varied among species, pathway-level analyses consistently revealed coordinated modulation of carbon–nitrogen metabolic networks. In reproductive tissues, TFMSA induced tissue-specific metabolic changes. In cowpea anthers, proline was the only metabolite significantly altered and was strongly depleted, whereas in floral tissues several amino acids, including phenylalanine and tyrosine, were accumulated. Pathway analysis revealed altered amino acid metabolism, suggesting that systemic metabolic responses accompanied the proline reduction in anthers. These findings indicate that TFMSA induces male sterility through coordinated metabolic reprogramming across tissues and species, leading to depletion of key metabolites required for pollen development. This study provides a metabolic framework for understanding CHA-induced male sterility and highlights TFMSA as a powerful tool for probing metabolic regulation of pollen development. Full article

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks. Full article

Alternative splicing (AS) is a major post-transcriptional regulatory mechanism that greatly expands transcriptomic and proteomic diversity in plants. Recent studies have demonstrated that AS dynamically regulates gene expression during plant development and under diverse environmental conditions through isoform-specific modulation of transcript stability, translation efficiency, protein localization, and signaling pathways. In this review, we summarize recent advances in understanding the roles of AS in plant development and abiotic stress responses. Mechanistically, splice site selection is regulated through coordinated interactions among cis-regulatory elements, RNA-binding proteins, RNA secondary structures, transcriptional kinetics, chromatin organization, and spliceosomal dynamics. AS plays critical roles in various developmental processes, including seed germination, vegetative growth, flowering transition, and senescence, while also contributing to plant adaptation to abiotic stresses such as osmotic, temperature, and oxidative stresses. Particular emphasis is placed on the diverse regulatory outcomes of AS, including isoform-specific protein functions, AS-coupled nonsense-mediated decay, transcript stability control, and context-dependent isoform switching. We further discuss the varying levels of experimental evidence supporting reported AS events, ranging from transcriptome-wide observations to genetically and biochemically validated isoform functions. Moreover, recent advances in long-read sequencing, single-cell transcriptomics, proteogenomics, and genome-engineering technologies are accelerating the functional characterization of splice isoforms and uncovering the complexity of AS-mediated regulatory networks. Collectively, these advances highlight AS as a central mechanism coordinating plant developmental plasticity and environmental adaptation. Full article

The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Perspective, we introduce XAI2Brain as a conceptual framework for brain–AI alignment, positioning mechanistic interpretability as an intermediate layer connecting neural network representations, human understanding, and neuroscience-inspired AI design. Rather than viewing XAI solely as a post hoc transparency tool, we emphasize its emerging role in enabling mechanistic analysis of internal model representations, concept-level reasoning, and interactive human–AI alignment. We define XAI2Brain as a multi-level conceptual framework rather than a deployable system, explicitly aimed at structuring brain–AI alignment across representation-level, mechanism-level, and interaction-level perspectives. We survey the evolution of XAI methodologies—from feature attribution and concept-based explanations to mechanistic and human-centric interpretability approaches—and discuss how these methods may support bidirectional knowledge transfer between AI systems and cognitive neuroscience. Importantly, we adopt a cautious stance on brain–AI analogy, explicitly recognizing that artificial neural representations are not equivalent to biological neural representations, and instead focusing on functional and informational correspondences rather than structural equivalence. Unlike conventional human-in-the-loop or reinforcement learning from human feedback paradigms that primarily optimize behavioral outputs, XAI2Brain focuses on cognitively interpretable and mechanistically grounded alignment between AI systems and human reasoning processes. This alignment promotes interactive human-in-the-loop intelligence, empowering humans to comprehend, guide, and refine AI systems, while enabling AI systems to better interpret human instructions, intentions, and contextual reasoning. We further discuss the challenges of scaling explainability to large generative and multimodal models, including issues of interpretability robustness, cognitive compatibility, evaluation, and ethical accountability. We also highlight key limitations of current mechanistic interpretability methods, including explanation instability, representation superposition, and lack of causal guarantees, underscoring that these challenges remain open research problems. Rather than proposing a complete artificial brain architecture, this Perspective outlines a research roadmap toward more interpretable, adaptive, and neuroscience-inspired AI systems capable of supporting future brain–AI integration and collaborative intelligence. We additionally clarify that this work follows a narrative perspective review methodology with structured thematic synthesis of the literature. By framing explainability as a bridge between mechanistic AI understanding, cognitive science, and human-centered interaction, XAI2Brain highlights the importance of interpretable alignment for the next generation of brain-inspired AI systems. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

Introduction: Freshwater systems are increasingly being impacted by a plethora of pressures. Freshwater fish are thus periled, urging the need to investigate the drivers of population decrease to better counteract them, in order to provide some conservation relief to these pressured species. Methodology: To facilitate freshwater research, the Dammed Fish Project developed a series of free tools that simplify procedures and facilitate the access of correct data. Results: RivTool+ is a free software that evolved from RivTool (used in over 75 countries) and that integrates new functions and acts as a tool hub to host additional software apps. The computing engine of RivTool, that allows along the river network calculations and summarizations, is now able to be used by new tools. RivConnect—River network connectivity app that allows graph-based quantification of structural and functional connectivity, using several metrics and understanding network directionality. RivFish—App that contains the corrected, spatially and taxonomically, occurrence, at the basin and sub-basin level, of more than 600 native freshwater fish species of Europe. RivOpt—Optimization tool that allows for river network connectivity restoration optimization. RivOpt accounts for conflicting multiple objectives and is able to deal with different restoration actions for each barrier (removal, partial removal, fishway construction and retrofitting or no action). Conclusions: Dammed Fish tools facilitate research procedures and access to verified data, improving the information baseline, increasing the accuracy of results and accelerating research. Thus, it contributes to an improved understanding of the mechanisms controlling species vulnerability and contributes to their conservation. Full article

Background: Soil mulching is a widely adopted agricultural practice known to regulate soil microclimate and enhance crop productivity; yet the biochemical mechanisms by which intact plastic and biodegradable mulch films influence soil nitrogen (N) cycling at the metabolic pathway level remain largely unexplored. Understanding these nitrogen transformation pathways is critical for assessing the long-term impacts of mulching materials on soil microbial communities, soil health, and sustainable agricultural management. This study focuses on the biochemical effects of intact mulch film application on soil N metabolism. Methods: N cycle-related soil metabolites were profiled using GC–MS/MS and MALDI TOF/TOF MS and then integrated with multivariate statistical modelling and pathway-level metabolic network perturbation analysis to compare conventional plastic and biodegradable plastic mulch film application against unmulched controls. Results: A panel of 62 KEGG-annotated N-cycle metabolites was profiled, and material-dependent metabolome separation was confirmed by OPLS-DA (R²Y 0.893–0.956; Q² 0.546–0.786). Both mulching materials significantly perturbed soil N-metabolite pools but differed in terms of pathway identity, magnitude, and directionality. Conventional plastic mulching caused the greatest disruption—near-complete suppression of N-storage and stress-adaptation pools (NES of −1.16; impact score of 10.01) and severe impairment of aspartate-centred metabolism—with L-aspartate identified as a critical stoichiometric hub. Biodegradable mulching material imposed a distinct profile dominated by inhibition of branched-chain amino acid catabolism and lysine degradation, with L-pipecolate as a treatment-specific critical impact node. Conclusions: These findings support that mulching material choice is a primary determinant of soil N-cycling biochemistry. The observed metabolite-level perturbations are suggestive of potential consequences for nitrogen retention. Though this inference is based on metabolite pool size differences and network topology metrics rather than directly measured process rates, it should therefore be interpreted with appropriate caution. Full article

Ecological zoning is important for understanding spatial heterogeneity and supporting landscape-level management. However, existing approaches rarely integrate ecosystem service supply with ecological risk, and their underlying nonlinear relationships remain insufficiently explored. This study aims to develop an integrated framework linking ecosystem service value (ESV) and landscape ecological risk (LER) based on a two-dimensional quadrant model. This framework integrates ESV and LER from complementary benefit–risk perspectives, advancing ecological zoning beyond single-indicator approaches. Using the Miyun Reservoir Basin as a case study, multi-source data from 2000 to 2020 were used to quantify ESV and LER and to examine their spatiotemporal dynamics. The ESV-LER framework was applied to identify ecological functional zones. In addition, the XGBoost-SHAP model combined with the Geographical Detector was used to explore the nonlinear effects and interactions of natural and anthropogenic drivers. ESV showed a “decline-recovery” trend, whereas LER exhibited an opposite “decrease-increase” pattern. Areas with both high ESV and high LER were mainly distributed around the reservoir and river networks, suggesting a spatial mismatch between ecological value and risk. Ecological improvement and conservation zones accounted for approximately 79% of the basin, while ecological risk prevention zones expanded over time, indicating increasing human disturbance. NDVI was identified as a dominant factor with dual effects, enhancing ESV while reducing LER, whereas population density and NPP exhibited nonlinear threshold effects that increased ecological risk. Overall, this study advances ecological zoning by integrating functional value and risk perspectives while explicitly revealing their nonlinear drivers. The proposed framework provides a transferable and interpretable approach for watershed-scale ecological management and supports more targeted and differentiated governance strategies. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article