Search Results (164)

Although the clownfish, Amphiprion ocellaris (A. ocellaris), is a popular ornamental marine fish worldwide, the mechanisms underlying color pattern variation remain unclear. Given that the Platinum-type clownfish, nearly entirely white, has high economic value, understanding the biological mechanism that accounts for the difference between orange and white colors in A. ocellaris is crucial. To investigate these coloration differences, we performed RNA sequencing analysis and identified differentially expressed genes (DEGs) by comparing white and orange skin samples from three A. ocellaris individuals. A total of 76 DEGs were detected, including 56 downregulated and 20 upregulated genes. DEG sequences were annotated using Danio rerio and Stegastus partitus as reference species, selecting the best hit based on the lowest E-value. A protein–protein interaction (PPI) network and Gene Ontology biological process terms were additionally analyzed. Several DEGs previously reported to be associated with pigmentation, including hpdb, cldn11b, sfrp5, slc2a9, slc2a11b, si:ch211-256m1.8, fhl2, rab38, and ttc39b were identified. Based on the functions of these DEGs, it is inferred that leucophores and xanthophores contribute to both white and orange coloration by modulating related genes, including slc2a11b and slc2a9. Additionally, sfrp5, sost, and sp7 genes were identified to interact with each other in the PPI analysis, with sfrp5 and sost being associated with the Wnt signaling pathway, which contributes to melanocyte specification and osteoblast differentiation. Based on these findings, we propose sost and sp7 as candidate genes that might provide insights relevant to extreme white pigmentation phenotypes, such as those observed in Platinum-type clownfish. For a clearer understanding, further studies integrating quantitative genetics and functional analyses are required. Full article

Road technical condition assessment and maintenance decision-making rely heavily on technical standards whose clauses, computational formulas, and decision logic are often expressed in unstructured formats, leading to fragmented knowledge representation, isolated indicator calculation procedures, and limited interpretability of decision outcomes. To address these challenges, a semantic framework with executable reasoning and computation components, Road Performance and Maintenance Ontology (RPMO), was developed, composed of a core ontology, an assessment ontology, and a maintenance ontology. The framework formalized clauses, computational formulas, and decision rules from standards and integrated semantic web rule language (SWRL) rules with external computational programs to automate distress identification and the computation and write-back of performance indicators. Validation through three use case scenarios conducted on eleven expressway asphalt pavement segments demonstrated that the framework produced distress severity inference, indicator computation, performance rating, and maintenance recommendations that were highly consistent with technical standards and expert judgment, with all reasoning results traceable to specific clauses and rule instances. This research established a methodological foundation for semantic transformation of road technical standards and automated execution of assessment and decision logic, enhancing the efficiency, transparency, and consistency of maintenance decision-making to support explicit, reliable, and knowledge-driven intelligent systems. Full article

This study presents a systematic review of ontology–AI integration for construction image understanding, aiming to clarify how ontologies enhance semantic consistency, interpretability, and reasoning in AI-based visual analysis. Construction sites involve highly dynamic and unstructured conditions, making image-based hazard detection and situation assessment both essential and challenging. Ontology-based frameworks offer a structured semantic layer that can complement deep learning models; however, most existing studies adopt ontologies only as post-processing mechanisms rather than embedding them within model training or inference workflows. Following PRISMA 2020 guidelines, a comprehensive search of the Web of Science Core Collection (2014–2025) identified 587 publications, of which 152 met the eligibility criteria, and 16 explicitly addressed construction image data. Topic modeling revealed five functional objectives—regulatory compliance, hazard reasoning, decision support, knowledge reuse, and sustainability—and four primary data modalities: BIM, text, image, and sensor data. Two dominant integration patterns were observed: training-stage and output-stage enhancement. While quantitative performance improvements were modest, qualitative gains were consistent across studies, including reduced false positives, improved interpretability, and enhanced situational understanding. Persistent gaps were identified in standardization, scalability, and real-world validation. This review provides the first structured synthesis of ontology–AI research for construction image understanding and offers an evidence-based research agenda that links observed limitations to actionable directions for semantic AI in construction. Full article

Background/Objectives: The clinical adoption of AI in radiology requires models that balance high accuracy with interpretable, anatomically plausible reasoning. This study presents an integrated diagnostic framework that addresses this need by unifying a hybrid deep-learning architecture with explicit anatomical guidance and neuro-symbolic inference. Methods: The proposed system employs a dual-path model: an enhanced EfficientNetV2 backbone extracts hierarchical local features, whereas a refined Vision Transformer captures global contextual dependencies across the thoracic cavity. These representations are fused and critically disciplined through auxiliary segmentation supervision using CheXmask. This anchors the learned features to lung and cardiac anatomy, reducing reliance on spurious artifacts. This anatomical basis is fundamental to the interpretability pipeline. It confines Gradient-weighted Class Activation Mapping (Grad-CAM) visual explanations to clinically valid regions. Then, a novel neuro-symbolic reasoning layer is introduced. Using a fuzzy logic engine and radiological ontology, this module translates anatomically aligned neural activations into structured, human-readable diagnostic statements that explicitly articulate the model’s clinical rationale. Results: Evaluated on the NIH ChestX-ray14 dataset, the framework achieved a macro-AUROC of 0.9056 and a macro-accuracy of 93.9% across 14 pathologies, with outstanding performance on emphysema (0.9694), hernia (0.9711), and cardiomegaly (0.9589). The model’s generalizability was confirmed through external validation on the CheXpert dataset, yielding a macro-AUROC of 0.85. Conclusions: This study demonstrates a cohesive path toward clinically transparent and trustworthy AI by seamlessly integrating data-driven learning with anatomical knowledge and symbolic reasoning. Full article

Background/Objectives: Parkinson’s disease (PD) is an adult-onset neurodegenerative disorder whose pathogenesis is still not completely understood. Several lines of evidence suggest that alterations in epigenetic architecture may contribute to the development of this condition. Here, we present a pilot DNA methylation study from peripheral blood in a cohort of Sicilian PD patients and matched controls. Peripheral tissue analysis has previously been shown to reflect molecular and functional profiles relevant to neurological diseases, supporting their validity as a proxy for studying brain-related epigenetic mechanisms. Methods: We analyzed 20 PD patients and 20 healthy controls (19 males and 21 females overall), matched for sex, with an age range of 60–87 years (mean 72.3 years). Peripheral blood DNA was extracted and processed using the Illumina Infinium MethylationEPIC v2.0 BeadChip, which interrogates over 935,000 CpG sites across the genome, including promoters, enhancers, CpG islands, and other regulatory elements. The assay relies on sodium bisulfite conversion of DNA to detect methylation status at single-base resolution. Results: Epigenome-wide association study (EWAS) data allowed for multiple levels of analysis, including immune cell-type deconvolution, estimation of biological age (epigenetic clocks), quantification of stochastic epigenetic mutations (SEMs) as a measure of epigenomic stability, and differential methylation profiling. Immune cell-type inference revealed an increased but not significant proportion of monocytes in PD patients, consistent with previous reports. In contrast, epigenetic clock analysis did not reveal significant differences in biological age acceleration between cases and controls, partially at odds with earlier studies—likely due to the limited sample size. SEMs burden did not differ significantly between groups. Epivariations reveal genes involved in pathways known to be altered in dopaminergic neuron dysfunction and α-synuclein toxicity. Differential methylation analysis, however, yielded 167 CpG sites, of which 55 were located within genes, corresponding to 54 unique loci. Gene Ontology enrichment analysis highlighted significant overrepresentation of pathways with neurological relevance, including regulation of synapse structure and activity, axonogenesis, neuron migration, and synapse organization. Notably, alterations in KIAA0319, a gene involved in neuronal migration, synaptic formation, and cortical development, have previously been associated with Parkinson’s disease at the gene expression level, while methylation changes in FAM50B have been reported in neurotoxic and cognitive contexts; our data suggest, for the first time, a potential epigenetic involvement of both genes in Parkinson’s disease. Conclusions: This pilot study on a Sicilian population provides further evidence that DNA methylation profiling can yield valuable molecular insights into PD. Despite the small sample size, our results confirm previously reported findings and highlight biological pathways relevant to neuronal structure and function that may contribute to disease pathogenesis. These data support the potential of epigenetic profiling of peripheral blood as a tool to advance the understanding of PD and generate hypotheses for future large-scale studies. Full article

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This study aims to address this challenge by developing a semantic collaborative filtering recommendation system designed to enhance dataset discovery in geoportals through the analysis of implicit user interactions. The system captures users’ search queries, viewed datasets, downloads, and applied filters to infer feedback and organize it into a user–item matrix. Because interaction data are typically sparse, semantic user clustering is applied to mitigate this limitation by grouping users with semantically related interests through hierarchical relationships represented in the Simple Knowledge Organization System (SKOS). However, as users often need complementary datasets to complete specific tasks, association rule mining is employed to identify co-occurrence patterns in search histories and enhance task-related result diversity. The final recommendation scores are then computed by factorizing the user–item matrix with Alternating Least Squares (ALS), using cosine similarity on the latent user vectors to identify nearest neighbors, and applying a standard user-based neighborhood prediction model to rank unseen datasets. The system is implemented within an existing ontology-based geoportal as a standalone, configurable component, requiring only access to user interaction logs and dataset identifiers. Evaluation using precision, recall, and Precision@5 demonstrates that increasing user interactions improves recommendation performance by strengthening behavioral evidence used for ranking. The findings indicate that integrating semantic relationships and behavioral patterns can strengthen dataset discovery in geoportals and complement conventional metadata-based search mechanisms. Full article

Background: Focal cortical dysplasia (FCD) is a prevalent cause of drug-resistant epilepsy, but a comprehensive understanding of its pathogenesis at a cellular resolution remains limited. Previous transcriptomic studies, often constrained by bulk tissue analysis, have been unable to dissect the cell-type-specific contributions to epileptogenesis. Methods: We performed scRNA-seq on cortical tissues from one surgical patient with FCD type II and one matched control. Cell clustering, annotation, and identification of differentially expressed genes (DEGs) were conducted using standard Seurat workflow. We focused on the molecular alterations in three major glial cell types: astrocytes, microglia, and oligodendrocytes. To functionally interpret the DEGs, we performed enrichment analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: Our profiling revealed a profoundly reconstituted cellular ecosystem in the FCD cortex. We found a marked expansion of microglia (65.57% vs. 47.02%; a ~39% relative increase) and astrocytes (10.98% vs. 4.11%; a ~167% relative increase), alongside a severe depletion of oligodendrocytes (8.12% vs. 30.63%; a ~73% relative decrease). Critically, a core set of 128 differentially expressed genes (DEGs) was shared across these glial populations, featuring consistent upregulation of RAC1 and downregulation of ATP5F1D, pointing to convergent pro-inflammatory and mitochondrial dysfunction pathways. Enrichment analyses further demonstrated a coordinated engagement of neuroinflammatory pathways, most notably IL-17 signaling. Subsequent cell–cell communication inference revealed a broad attenuation of intercellular signaling, with a 35% reduction in interaction numbers, indicating a breakdown of coordinated cellular crosstalk. Conclusions: This exploratory single-cell study provides preliminary evidence of a convergent glial pathology in FCD, characterized by shared molecular disruptions in inflammation and metabolism. Our findings highlight RAC1 and IL-17 signaling as potentially actionable pathways, warranting further investigation into their therapeutic potential for mitigating epileptogenesis in FCD. Full article

With the increasing complexity of Autonomous Vehicle networks, enhanced cyber security has become a critical challenge. Traditional security techniques often struggle to adapt dynamically to evolving threats. Overcoming these limitations, this paper presents a novel domain ontology to structure knowledge concerning AV security threats, intrusion characteristics, and corresponding mitigation techniques. Unlike previous work, which mainly focused on static classifications or direct integration within Intrusion Detection Systems, our approach has the distinctive feature of creating a formalized and coherent semantic representation. The ontology was designed using Protégé 4.3 and Web Ontology Language (OWL), modeled from the core cyber security concepts of AVs, and it provides a more nuanced threat classification and significantly superior automated reasoning capability. An important feature of our design is that the ontology formalization was done independently of any real-time IDS integration. A PoC was carried out to prove that the ontology could select the most appropriate method of mitigation, using as input the output of machine-learning-based IDS; SPARQL queries retrieve mitigation instance, type, and effectiveness. This design choice enables us to concentrate strictly on validating the foundational semantic coherence and reasoning power of the knowledge structure, hence providing a robust and reliable analytical framework for further reactive and predictive security applications. The experimental evaluation confirms enhanced effectiveness in knowledge organization and reduces inconsistencies in security threat analysis. Specifically, class classification was performed in 1.049 s, while consistency check required just 0.044 s, hence validating the model’s robustness against classification principles and concept inferences. This work thus paves the way for the development of more intelligent and adaptive security frameworks. In the future, research will be focused on the integration with real-time security monitoring and IDS frameworks and on the study of optimization techniques, such as genetic algorithms, to improve the real-time selection of the countermeasures. Full article

Forest fires have become increasingly frequent and severe due to climate change and intensified human activities, posing critical challenges to ecological security and emergency management. Despite the availability of abundant environmental, spatial, and operational data, these resources remain fragmented and heterogeneous, limiting the efficiency and accuracy of fire prediction and response. To address this challenge, this study proposes a Semantic Digital Twin-Driven Framework for integrating multi-source data and supporting forest fire prediction and response. The framework constructs a multi-ontology network that combines the Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) ontologies for sensor and observation data, the GeoSPARQL ontology for geospatial representation, and two domain-specific ontologies for fire prevention and emergency response. Through systematic data mapping, instantiation, and rule-based reasoning, heterogeneous information is transformed into an interconnected knowledge graph. The framework supports both semantic querying (SPARQL) and rule-based reasoning (SWRL) to enable early risk alerts, resource allocation suggestions, and knowledge-based decision support. A case study in Sichuan Province demonstrates the framework’s effectiveness in integrating historical and live data streams, achieving consistent reasoning outcomes aligned with expert assessments, and improving decision timeliness by enhancing data interoperability and inference efficiency. This research contributes a foundational step toward building intelligent, interoperable, and reasoning-enabled digital forest systems for sustainable fire management and ecological resilience. Full article

This paper investigates an approach to studying analytic relations (identity, similarity, borrowing, etc.) between literary characters using properties and, in particular, properties that are interpretively considered as diagnostic. In our proposal, properties serve as interpretative tools rather than strict ontological features. Unlike most ontological theories of literary characters developed in analytic philosophy, our study focuses on how real-world interpreters construct textual meaning while remaining agnostic about the ontological status of literary entities (ficta, in a more general sense). By integrating perspectives from literary criticism, philosophy, and formal methods, we explore how scholars infer relations between characters through textual evidence, common knowledge, and interpretive frameworks. This research aims at refining methodological approaches to character analysis and at contributing to broader discussions on literary interpretation and fictionality. Full article

Background/Objectives: The mTOR serine/threonine kinase coordinates protein translation, cell growth, and metabolism, and its dysregulation promotes tumorigenesis. We present a reproducible, pan-cancer, network-aware framework that integrates curated resources with genomics to move beyond pathway curation, yielding falsifiable hypotheses and prioritized candidates for mTOR axis biomarker validation. Materials and Methods: We assembled MTOR-related genes and interactions from GeneCards, KEGG, STRING, UniProt, and PathCards and harmonized identifiers. We formulated a concise working model linking genotype → pathway architecture (mTORC1/2) → expression-level rewiring → phenotype. Three analyses operationalized this model: (i) pan-cancer alteration mapping to separate widely shared drivers from tumor-specific nodes; (ii) expression-based activity scoring to quantify translational/nutrient-sensing modules; and (iii) topology-aware network propagation (personalized PageRank/Random Walk with Restart on a high-confidence STRING graph) to nominate functionally proximal neighbors. Reproducibility was supported by degree-normalized diffusion, predefined statistical thresholds, and sensitivity analyses. Results: Gene ontology analysis demonstrated significant enrichment for mTOR-related processes (TOR/TORC1 signaling and cellular responses to amino acids). Database synthesis corroborated disease associations involving MTOR and its partners (e.g., TSC2, RICTOR, RPTOR, MLST8, AKT1 across selected carcinomas). Across cohorts, our framework distinguishes broadly shared upstream drivers (PTEN, PIK3CA) from lineage-enriched nodes (e.g., RICTOR-linked components) and prioritizes non-mutated, network-proximal candidates that align with mTOR activity signatures. Conclusions: This study delivers a transparent, pan-cancer framework that unifies curated biology, genomics, and network topology to produce testable predictions about the mTOR axis. By distinguishing shared drivers from tumor-specific nodes and elevating non-mutated, topology-inferred candidates, the approach refines biomarker discovery and suggests architecture-aware therapeutic strategies. The analysis is reproducible and extensible, supporting prospective validation of prioritized candidates and the design of correlative studies that align pathway activity with clinical response. Full article

This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional object detection approaches that rely on post-processing heuristics to establish relationships between the detected elements, COG embeds contextual understanding directly into the detection process by treating spatially and functionally related objects as unified semantic entities. We demonstrate this approach in the context of Cyber-Physical Security Systems (CPPS) assessment, where the same symbol may represent different security devices across different designers and projects. Our proof-of-concept implementation using YOLOv8 achieves robust detection of legend components (mAP50 ≈ 0.99, mAP50–95 ≈ 0.81) and successfully establishes symbol–label relationships for automated security asset identification. The framework introduces a new ontological class—the contextual COG class that bridges atomic object detection and semantic interpretation, enabling intelligent sensing systems to perceive context rather than infer it through post-processing reasoning. This proof-of-concept appears to validate the COG hypothesis and suggests new research directions for structured visual understanding in smart sensing environments, with applications potentially extending to building automation and cyber-physical security assessment. Full article

This study develops an ontology-based decision support framework to enhance sustainable polymer recycling within the circular economy. The framework, constructed in Protégé (OWL 2), systematically captures polymer categories with emphasis on polyethylene terephthalate (PET), polylactic acid (PLA), and rigid polyvinyl chloride (PVC) as well as recycling processes, waste classifications, and sustainability indicators such as carbon footprint. Semantic reasoning was implemented using the Semantic Web Rule Language (SWRL) and SPARQL Protocol and RDF Query Language (SPARQL) to infer optimal material flows and sustainable pathways. Validation through a UK industrial case study confirmed both the framework’s applicability and highlighted barriers to large-scale recycling, including performance gaps between virgin and recycled polymers. The comparative analysis showed carbon footprints of 2.8 kg CO₂/kg for virgin PET, 1.5 kg CO₂/kg for PLA, and 2.1 kg CO₂/kg for PVC, underscoring material-specific sustainability challenges. Validation through a UK industrial case study further highlighted additive complexity in PVC as a major barrier to large scale recycling. Bibliometric and thematic analyses conducted in this study revealed persistent gaps in sustainability metrics, lifecycle assessment, and semantic support for circular polymer systems. By integrating these insights, the proposed framework provides a scalable, data-driven tool for evaluating and optimising polymer lifecycles, supporting industry transitions toward resilient, circular, and net-zero material systems. Full article

As cities confront intensifying environmental challenges and increasing expectations for sustainable governance, extending Environmental, Social, and Governance (ESG) evaluation frameworks to the urban scale has become a pressing need. However, existing ESG systems are typically designed for corporate contexts, lacking city-specific indicators, integrated data representations, and reliable ESG information with high spatial and temporal resolution for informed decision-making. This study proposes a comprehensive ESG evaluation framework tailored to green cities, which consists of three core components: (1) The construction of a green-oriented ESG indicator system with an expert-informed weighting system; (2) the design of a GIS-BIM-IoT integrated ontology that semantically aligns spatial, infrastructure, and observational data with ESG dimensions; and (3) the implementation of a web-based data integration and visualization platform that dynamically aggregates and visualizes ESG insights. A case study involving a primary school and an air quality monitoring station in Hong Kong demonstrates the system’s capability to infer material recycling rates and pollution concentration scores using ontology-driven reasoning and RDF-based knowledge graphs. The results are rendered in an interactive 3D urban interface, supporting real-time, multi-scale ESG evaluation. This framework transforms ESG assessment from a static reporting tool into a strategic asset for transparent, adaptive, and evidence-based urban sustainability governance. Full article