Search Results (1,711)

Abdominal aortic aneurysm (AAA) is a progressive and often asymptomatic vascular disease associated with high mortality after rupture, but reliable circulating biomarkers for noninvasive detection remain limited. We aimed to identify and validate plasma protein biomarkers for AAA using an integrated proteomics-based approach. Plasma samples from 22 patients with AAA and 22 healthy controls were analyzed through data-independent acquisition (DIA) mass spectrometry. Differentially expressed proteins were subjected to bioinformatic analyses, including Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein–protein interaction, and weighted gene coexpression network analyses. Candidate biomarkers were selected on the basis of differential abundance, diagnostic performance, and biological relevance and subsequently validated by enzyme-linked immunosorbent assay in an independent cohort comprising 93 patients with AAA and 83 non-AAA controls. DIA proteomics identified 111 differentially abundant proteins, revealing enrichment of pathways related to mitochondrial respiration, oxidative stress, inflammation, extracellular matrix remodeling, and proteostasis. Among the candidates, plasma CHRDL1 levels were significantly reduced, whereas OGN and CCL18 levels were significantly elevated in patients with AAA; these findings were consistently confirmed in the validation cohort. A combined three-protein model demonstrated strong diagnostic performance, with an area under the receiver operating characteristic curve of 0.890. These findings identify CHRDL1, OGN, and CCL18 as promising plasma biomarkers for AAA detection and further highlight mitochondrial dysfunction, chronic inflammation, ECM remodeling, and dysregulated proteostasis as key molecular features of AAA. Full article

Soybean root rot caused by Fusarium graminearum is an important soil-borne disease. It hinders seedling establishment and ultimately reduces soybean yield. Resistant germplasm and reliable molecular markers are therefore needed for resistance breeding. In this study, 336 soybean accessions were evaluated for resistance to F. graminearum root rot using the disease severity index (DSI), which ranged from 5.71 to 100.00 across the association panel. Genome-wide association analysis was performed using resequencing-based single nucleotide polymorphism (SNP) data with mixed linear model (MLM) and Fixed and random model Circulating Probability Unification (FarmCPU) models, which detected 117 and 113 candidate resistance-associated SNPs, respectively. Among these, 105 shared SNPs were used to define candidate genomic intervals containing 247 annotated genes. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, functional annotation, and allelic-effect analysis, six candidate genes and their associated exonic SNPs were prioritized. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showed infection-responsive expression patterns for all six candidate genes, with Glyma.17g202500 and Glyma.18g266700 showing stronger induction in the resistant accession. Two SNPs in these genes were converted into Kompetitive allele-specific PCR (KASP) assays. KASP-S17_32244510 and KASP-S18_55105706 were successfully developed for genotype screening, with screening efficiencies of 73.08% and 74.29%, respectively. These findings identify useful genetic targets and molecular markers for improving soybean resistance to root rot caused by F. graminearum. Full article

Deep neural networks have achieved substantial success in image, text, and signal analysis, but their advantage is less consistent for heterogeneous tabular data, where tree-based ensemble methods often remain strong baselines. This study proposes CANON (Cross-Attention Neuro-symbolic Ontology Network), a neuro-symbolic architecture that integrates a hierarchical a priori concept ontology with a modular mixture-of-experts mechanism. CANON is designed to combine data-driven representation learning with explicit domain structure and to reduce the influence of irrelevant or weakly informative features. The architecture was evaluated on two clinical tabular cohorts—848 patients with ANCA-associated vasculitis described by 142 features, and 200 patients assessed for coronary artery stenosis described by 593 features—using stratified 8-fold cross-validation, and was compared with tree-based ensembles, dedicated tabular deep learning models, and classical neural architectures. CANON achieved the highest mean AUC on both cohorts (0.9426 and 0.9484). Two findings are reported. First, CANON significantly outperformed in AUC all four non-tree baselines included in the paired statistical analysis: all eight paired comparisons against GaussianNB, FT-Transformer, TabNet and LSTM were significant on both cohorts and favored CANON in 8 of 8 folds, with FT-Transformer and TabNet tuned separately for each cohort under an identical budget of 20 Optuna trials each. Second, CANON performed comparably to the tree-based ensembles, significantly outperforming Random Forest on the coronary artery stenosis cohort ($\Delta =+0.124$, $p=0.004$), while the remaining comparisons against Random Forest and XGBoost did not reach significance. An ablation study comprising 40 cross-validation folds per variant shows that the semantic organization of features into concepts and the non-linear feature tokenizer contribute measurably to the harder, higher-dimensional cohort, whereas the directional fusion mechanism does not improve predictive performance and is retained for its interpretability role. These findings indicate that ontology-guided neural architectures can provide a competitive and interpretable basis for clinical decision support and may also be useful in other regulated domains in which predictive models must remain consistent with domain-specific knowledge. Full article

This systematic review explores the multifaceted factors influencing African Australian migrant women’s decisions to remain in family violence relationships. It critically examines how existing service frameworks marginalise women who stay and advocates for culturally safe, trauma-informed, and harm-reduction programs. The study calls for a significant policy and practice shift to recognise diverse survival strategies beyond conventional notions of “leaving”. Guided by a critical realist ontological approach, the review employs thematic analysis of qualitative data to illuminate the complex interplay of social, cultural, legal, and economic factors shaping women’s choices. Findings reveal that current service models, predominantly based on a “leave to be safe” paradigm, fail to address intersecting barriers faced by African Australian migrant women. These include precarious visa status, language barriers, and fear of child removal, which compel many to remain in violent environments. The study highlights the unique challenges posed by insecure migration status, which intensifies family violence and impedes help-seeking behaviours. This review identifies significant structural and intersectional barriers, critical policy gaps and proposes re-envisioning support pathways to better serve women who, for structural and personal reasons, cannot or choose not to leave abusive relationships. It underscores the need for culturally safe, harm-reduction strategies and systemic reforms to dismantle structural inequalities embedded in immigration and family violence responses. Full article

Planning debates increasingly recognise that climate adaptation, community wellbeing, and tourism development cannot be treated as separate domains; yet existing place-based frameworks rarely integrate Indigenous governance, multidimensional wellbeing, and visitor dynamics within a single cohesive model. This article develops the Relational Resilient Place Framework as a conceptual model for understanding how culturally grounded landscapes can balance Indigenous authority, local community interests, and destination pressures. Drawing on mātauranga Māori (Indigenous knowledge) scholarship, therapeutic landscapes research, and the destination governance literature, the framework positions place as relational infrastructure. In this model, ecological vitality (mauri), collective wellbeing, cultural integrity, and visitor engagement are treated as interdependent rather than competing objectives. The framework is structured through six lenses: partnership integrity, cultural safety, community wellbeing, climate transition capacity, visitor–resident alignment, and long-term stewardship. The article establishes a conceptual basis for evaluating the theoretical conditions under which places function as resilient systems across rural, regional, and destination contexts. It argues that Indigenous governance principles provide a structural foundation for aligning wellbeing and tourism without reproducing extractive or growth-centric logics. In doing so, the framework demonstrates how Indigenous ontologies can move beyond symbolic inclusion to underpin resilient, non-extractive approaches to place-based planning. Full article

Large Language Models (LLMs) show considerable potential for materials-science question answering. However, LLM responses may still be affected by unsupported parametric associations, while dense Retrieval-Augmented Generation (RAG) can fragment relational evidence across text chunks. Moreover, general graph-based retrieval does not necessarily preserve the hierarchical relations and factual attributes required to resolve implicit material constraints. To address these limitations, we propose MCTD-KG, a multi-source heterogeneous knowledge graph integrated with a Knowledge-Enhanced RAG framework for complex material question answering. MCTD-KG adopts a Classification–Term–Data ontology to connect disciplinary taxonomies, domain concepts, semantic relations, and empirical records from toolbooks and the scientific literature. Through LLM-assisted knowledge extraction, entity normalization, and multi-source integration, the resulting graph contains more than 530,000 entities across three layers, including 61,768 text-extracted Term-layer entities. During inference, Dual-Channel Retrieval jointly retrieves query-relevant relational paths and associated material attributes, while an explicit semantic filtering stage screens candidate evidence against the query constraints. Evaluation on an expert-validated benchmark of 1577 questions shows that the proposed framework achieves an overall accuracy of 68.48%, compared with 17.40% for the zero-shot Pure LLM, 24.79% for the best Vanilla RAG setting, and 44.96% for GraphRAG. It also achieves 45.22% accuracy on four-hop questions, compared with 39.49% for GraphRAG. These results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering. Full article

The digital transformation of shrimp aquaculture in Ecuador has accelerated the adoption of traceability technologies to improve transparency, regulatory compliance, and supply chain coordination. However, persistent structural limitations—such as technological fragmentation, low interoperability, and inconsistent data quality—continue to constrain their effectiveness. This study develops an approach to managing informational uncertainty in traceability systems, grounded in information theory and operationalized through the Technological Management Model for Shrimp Production (TMMT-SP). Methodologically, the research follows an abductive systemic modeling approach, integrating a systematic literature review, structural analysis of the production system, and ontological modeling to identify and classify interdomain gaps. The findings show that informational uncertainty emerges as a structural property of the system, resulting from misalignments across informational, technological, and governance dimensions. These misalignments limit the coherence, reliability, and integration of traceability processes. In response, the study proposes a structural entropy-based framework (understood as an operational representation of systemic informational dispersion) to diagnose, prioritize, and address these gaps, shifting the focus from isolated technological adoption toward systemic coherence. This approach provides a conceptual and methodological basis for designing technology management strategies to reduce uncertainty in complex production systems. Full article

Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge. Full article

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

Mine ecological restoration involves multiple tasks, including ecological problem identification, restoration measure selection, and restoration effect evaluation. Relevant knowledge is widely scattered across academic literature and policy and regulatory documents, making it difficult to directly support structured queries, knowledge association, and measure screening. To address the insufficient organization of knowledge in mine ecological restoration and the unstable quality of triples extracted by large language models, this study proposes a large language model-driven workflow for knowledge graph construction and restoration measure decision support in mine ecological restoration. First, a weak ontology schema containing eight entity types and eight relation types was constructed, and a manually annotated dataset was developed based on 30 papers on mine ecological restoration. Second, multiple large language models were compared on the triple extraction task, and Kimi, which achieved the best overall performance, was selected for large-scale knowledge extraction. On this basis, a weak-ontology-guided triple quality control method, named WOTQC, was proposed to check the structural consistency of candidate triples in terms of entity type, relation type, relation direction, and entity orientation. DeepSeek was then introduced to perform verification based on original textual evidence. Subsequently, entity normalization and relation aggregation were conducted on the verified triples to construct a knowledge graph for mine ecological restoration. Path mining, restoration measure decision support, and external case validation were further performed around the “ecological problem–restoration measure–restoration effect” path. The experimental results show that Kimi-based initial extraction achieved an F1 score of 0.7286. After WOTQC processing, the F1 score increased to 0.7561, and after DeepSeek-based evidence verification, it further improved to 0.7862. The final knowledge graph contains 18,783 entity nodes and 23,149 relations, and identifies 3048 “ecological problem–restoration measure–restoration effect” paths. The external case validation shows that the integrated ranking method achieved 1.00 under both strict Hit@3 and strict Hit@5, with a path explanation coverage@5 of 0.7500. The ranking results of the knowledge graph showed good correspondence with the main engineering measures in an actual mine ecological restoration design scheme. The results indicate that weak ontology constraints and verification based on original textual evidence can improve the reliability of triple extraction, while entity normalization and relation aggregation can enhance the structural consistency of the knowledge graph. The constructed knowledge graph can provide traceable and interpretable knowledge support for knowledge organization, candidate restoration measure screening, and restoration scheme formulation in mine ecological restoration, thereby supporting more informed and evidence-based restoration decision-making and contributing to the long-term ecological recovery and sustainable management of degraded mining areas. Full article

This article examines the Filioque controversy as a thousand-year-old aporia in Roman Catholic–Orthodox relations, focusing on the 1995 Vatican Clarification, The Greek and Latin Traditions about the Procession of the Holy Spirit. It explores whether the dispute is a fundamental ontological disagreement regarding the hypostatic origin of the Spirit or a linguistic byproduct of translating the Greek term ekporeusis into the Latin processio. The study analyzes diverse theological responses to “the Clarification”. Roman Catholic perspectives investigate contemporary “reconceptions”—including simultaneous, nonsequential models and nonlinear geometric metaphors—while attempting to reconcile the “Monarchy of the Father” with the dogmatic authority of the Councils of Lyon II and Florence. Conversely, Orthodox theologians emphasize patristic rigor, insisting on a strict distinction between “Theology” (eternal life) and “Economy” (historical actions) while rejecting “Rahner’s Rule”. Ultimately, the research highlights that the unilateral canonical addition of the Filioque remains the primary ecclesiological barrier to unity. The article asserts that only deep, synodal dialogue, rather than administrative clarifications, can resolve this historical impasse and restore a common confession of faith. Full article

Background: Osteoarthritis (OA) is a prevalent degenerative joint disease with a complex molecular basis. This study aims to identify key molecules involved in OA pathogenesis, focusing on the role of acetylation-related gene expression. Methods: Public microarray datasets GSE82107 and GSE169077 were integrated to construct a differential expression landscape between OA patients and healthy controls. Acetylation-linked differentially expressed genes (acetylation-DEGs, ARDEGs) were extracted by intersecting DEGs with a curated set of acetyltransferases, deacetylases and acetylation substrates. A protein–protein interaction (PPI) network was built and subjected to LASSO-penalized regression to prioritise hub genes. Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Variation Analysis (GSVA) were performed to characterize biological themes. Immune infiltration was quantified with CIBERSORTx and single-sample Gene Set Enrichment Analysis (ssGSEA). Single-cell RNA-seq data (GSE216651) were employed for orthogonal validation. For experimental corroboration, synovial tissue was collected from OA patients undergoing arthroplasty; mRNA and protein levels of hub genes were determined by qRT-PCR, Western blot and immunofluorescence. The destabilisation of the medial meniscus (DMM) mouse model was used for in vivo verification. Results: Twenty-one high-confidence ARDEGs were identified. Analysis of the PPI network yielded ten hub nodes, six of which (EGR1, PFKFB3, HDAC4, MMP13, PDK4 and ACADL) retained non-zero coefficients in the least absolute shrinkage and selection operator (LASSO) model. Enrichment analyses implicated these genes in embryonic development, collagen-containing extracellular matrix remodeling and PI3K–Akt signaling. Immune infiltration analysis showed potential differences in immune cell abundance between OA and healthy controls. Single-cell dataset analysis verified the expression patterns of key genes in different cell types. Concordant dysregulation of EGR1, PFKFB3, HDAC4, MMP13 and PDK4 was observed at both mRNA and protein levels in human OA synovium and DMM mouse joints. Conclusion: This comprehensive analysis identified acetylation-related genes and analyzed their potential biological roles in OA. The identified ARDEGs may provide new insights into OA diagnosis and treatment. Full article

In heterogeneous multi-robot systems, allocating tasks efficiently requires determining whether each robot can actually carry out a given task. In existing multi-robot task allocation research, however, such task feasibility has typically been handled inside a particular optimizer or symbolic planner, while spatial traversability has been assessed against static criteria that cannot capture the changes induced by a robot’s loaded state. This paper proposes an ontology-based semantic feasibility reasoning method for heterogeneous multi-robot task allocation. The proposed method defines semantic models for robots, tasks, and places and applies hybrid reasoning, combining declarative reasoning with procedural evaluation, to determine multi-axis capability conditions and loaded-state place reachability. The reasoning result is formalized as an allocator-independent ReasonerOutput that serves as a common input for diverse allocation algorithms. In experiments spanning four scenarios over three fleet configurations and four allocators, together with an ablation study on 200 randomized instances at each of three problem scales, the proposed ReasonerOutput consistently functioned as a shared semantic feasibility constraint. The experiments further showed that both the fleet composition and the loaded state of the target item affect assignment feasibility. These results indicate that, for the static one-shot assignment setting evaluated here, the proposed method makes the task feasibility of heterogeneous robots explicit through semantic reasoning and allows allocators of differing algorithmic character to draw on this feasibility in common. Full article

Bioenergy (including biofuel) production and use are prerequisites for reducing greenhouse emissions and achieving sustainable development. In this work, on the basis of review and analysis of research achievements and elaborated ontologies in the field, an ontology of sustainable bioenergy is proposed. A methodology for its development includes: goals and scope definition, text corpus composition and extraction of the main concepts, controlled vocabulary and thesaurus building, ontology coding, reasoning, verification and querying. The established ontology has links to other related ontologies and is published in GitHub/Borydel/OSBE repository. Further extension of the sustainable bioenergy ontology is planned as well as its embedding in a dedicated repository. Full article

Integrating newly emerging terms into existing ontologies is a recurring maintenance problem in knowledge engineering, particularly in biomedical domains where terminology evolves faster than manual curation can accommodate. This paper addresses the placement of concepts that are absent from the target ontology—the out-of-knowledge-base setting—in which a textual mention must be assigned one or more insertion positions in the subsumption hierarchy rather than linked to an existing node. We propose a three-stage framework that extends the conventional retrieve-then-select paradigm with an intermediate stage of edge generation and enrichment, which expands the candidate set by traversing the local structure of the ontology. Stage 1 retrieves candidate edges using a fine-tuned bi-encoder trained with a max-margin objective; Stage 2 constructs and structurally enriches candidate edges; Stage 3 selects among them using either a fine-tuned cross-encoder or a large language model under explainable instruction tuning. We evaluate on two datasets derived from SNOMED CT, MM-S14-Disease and MM-S14-CPP, under a strict out-of-knowledge-base protocol. Fine-tuned pre-trained language models outperform zero-shot and instruction-tuned large language models on ranking accuracy, while the instruction-tuned configuration produces expert-auditable justifications at a modest cost in accuracy. On MM-S14-Disease, the strongest configuration places a correct insertion edge among the ten highest-ranked candidates for 38.7% of test mentions and recovers the complete gold edge set for 16.4%, against 26.1% and 9.2% for retrieval alone. The framework is positioned as decision support for ontology curators rather than as an autonomous ontology generator. Full article