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The processes of service discovery and composition are crucial tasks in application development driven by Web Services. However, with RESTful Web Service replacing SOAP-based Web Service as the dominant service-providing approach, the research on service discovery and composition should also shift its focus from SOAP-based Web Service to RESTful Web Service. The unstructured, resource-oriented and unified interface characteristics of RESTful Web Service pose challenges to its discovery and composition process. In this work, a framework for implementing RESTful Web Service discovery and automatic composition based on semantic technology is proposed. Firstly, the framework uses the OpenAPI Specification (OAS), which is extended by resource attributes, as the RESTful Web Service description specification, and then supports semantic-based matching discovery and automatic composition by attaching the concepts of domain ontology to the extended OAS. Secondly, the framework is fully adapted to REST features and provides a method for building service composition dependencies during registration, which is used to generate composition schemes during the service discovery process. Finally, the framework provides a discovery method that can return RESTful Web services to the requester in the form of single-point services or service composition schemes according to the magnitude of the semantic similarity with the requester's requirements. We applied the proposed methods to experiment with RESTful Web services in three different fields, and the results show that the methods effectively calculate the similarity between RESTful single-point Web services or composite Web services and service requests with the support of domain ontology. [ABSTRACT FROM AUTHOR]

Multi-example query over knowledge graphs allows users to provide multiple example fragments to express their intention, overcoming the shortage of single example query asks users to give a specific complete query example. Unfortunately, the existing multi-example query ignores the semantic relevance of entities, and query results are inconsistent in compactness, which causes it difficult for users to check their interesting results. Therefore, we first define a multi-example query over ontology-label knowledge graphs to facilitate users to express their query intentions and improve the semantic relevance of query results. Secondly, we propose a matching pattern to analyze users' query intention to prioritize the more compact query results to improve users' satisfaction. Specifically, we first use the OELIndex to filter the search space and improve query efficiency. Then, we construct a connected matching pattern according to query examples to get a more compact query result. After that, we select the pattern fragments with the smallest amount for priority matching by assessing the number of candidate sets, significantly reducing the isomorphic computations. Finally, extensive experiments are carried out on real data sets to verify the effectiveness and practicability of our proposed method. [ABSTRACT FROM AUTHOR]

Objective: The objective of this commentary is to advocate for a holistic, ontology‐based definition of bruxism. The intention is to synthesise the best aspects of current definitions into a structured ontological model, thereby refining and enhancing a comprehensive understanding of the full spectrum of bruxism. Materials and Methods: The commentary elaborates on the process of integrating these insights into a hierarchical ontology that aligns with ontological principles. Settings and Sample Population: Not directly applicable as this is a commentary. Results/Conclusion: The proposed ontology‐based definition of bruxism aims to clarify communication within the medical community and advance research by enabling a comprehensive ontology‐based classification of bruxism. By aligning with ontological principles, this approach aspires to act as a catalyst for further research, discussion and consensus in the field. [ABSTRACT FROM AUTHOR]

Additive Manufacturing increasingly becomes a viable alternative to conventional manufacturing technologies. However, the knowledge and awareness to achieve a ready-for-manufacturing and quality-ensured design of additively manufactured products are mostly lacking. Knowledge from the manufacturing process on how certain process variables affect the product requirements must already be considered in product design to assure and potentially enhance the quality a priori, thus saving time and cost-intensive iterations. Therefore, this knowledge must be front-loaded as a basis to suitably adapt product and process design simultaneously. This contribution therefore proposes a framework for the simultaneous product and process design synthesis of existing designs by providing insight knowledge about product and process design specific to Fused Layer Modeling. This is achieved via a consolidated ontology-based knowledge representation and its integration in a design environment. Thus, the aim, in contrast to most existing approaches, is to provide this knowledge on a product- and application-specific basis to parallelise product and process design for non-assembly mechanisms and exploit currently unused potential concerning the interaction between the different domains. This can shorten development times and also compensate for a lack of background knowledge in certain areas. A practical use case highlights the general procedure, benefits, and applicability. [ABSTRACT FROM AUTHOR]

The concept of Botho / Ubuntu emerges as a balancing paradigm poised to drive cognitive justice in psychological discourses. A paradigm is a universally recognized scientific model that represents a worldview of the nature of the world. There are enduring concerns about the privileging of Western European paradigms, ontologies, epistemologies, and axiologies over their African counterparts. In this article, we present the Botho / Ubuntu paradigm as a strong contender for the promotion, and humanization of epistemologies in psychology. The 59th annual conference of the Comparative and International Education Society (CIES) in Washington, DC in 2015, whose theme was " Ubuntu ! Imagining a Humanist Education Globally," as well as the World Council of Comparative Education Societies' (WCCES) recent book, Comparative Education for Global Citizenship, Peace and Shared Living through Ubuntu, are examples through which Ubuntu began to emerge as a paradigm poised to deliver cognitive and epistemic justice in the area such as psychology. We use Critical African Psychology as a lens through which we interrogate cognitive injustice. We conclude by demonstrating that Botho/Ubuntu paradigm might serve as a driver of cognitive justice in psychology and makes inroads into major discourses driven by African scholars. [ABSTRACT FROM AUTHOR]

Aim: To describe popular diet content visible on #intermittentfasting, #keto, and #lowcarb on adolescent social media accounts. Methods: An adolescent Instagram profile captured 200 'top' images from three popular diet hashtags (#intermittentfasting, #keto, and #lowcarb) across two timepoints. Images were coded using a pre‐determined ontology as food (core or discretionary; common foods/food groups), people (group, individual, before/after), or informative. Descriptive statistics were used to summarise these categories across hashtags. Results: At the time of first data collection, there were 3.8 million #intermittentfasting, 19 million #keto, and 22 million #lowcarb posts on Instagram. At the second timepoint there were 4.3 million #intermittentfasting, 21.5 million #keto, and 24.3 million #lowcarb posts. Images tagged #intermittentfasting were categorised as 44% food, 39% people, 10% information; #keto were 64% food, 28% people, 5% information; and #lowcarb were 69% food, 14% people, 16% information. Food images mostly depicted animal proteins (58.6% of #intermittentfasting; 62.9% of #keto; and 40.1% of #lowcarb). Images of people were individual (44%) or before/after (39%); mostly female (77%), of white (53%) ethnicity. Across all posts, 12.5% were linked to a commercial product/program, and 2.3% provided nutrition information. Conclusion: Diet‐related images visible when adolescents search #intermittentfasting, #keto, and #lowcarb on Instagram promote animal‐based foods with or without vegetables. These diet hashtags on Instagram do not provide nutrition information and are not helpful for young people searching for diet information online. [ABSTRACT FROM AUTHOR]

Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography–mass spectrometry (LC–MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16–18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

Traditional martial arts are treasures of humanity's knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries on the UNESCO list of intangible cultural heritage. In tackling the urgency of knowledge preservation through digital means, this project employs an ontology-based approach to model the conceptual realm of traditional martial arts. Accordingly, it creates the Martial Art Ontology (MAon) , a comprehensive domain ontology with an annotated data resource incorporating entities and relations from embodied, epistemic, and sociocultural facets. MAon underlines the significance of embodied qualities and addresses relevant dimensions, such as kinesthetics, techniques, mnemonics, and tactics, along with stylistic, interpretative, and ideological components. It features scholarly terminology developed through literature analysis, interviews with masters, and expert validations. The instantiation of MAon is realized through annotating three archetypal Southern Chinese styles, offering exhaustive descriptions concerning techniques, forms, principles, and form sets, amongst others. In summary, the reported approach encodes the manifold of martial arts into a structured vocabulary and an interlinked data resource, accessible to both human-reading and machine-operating applications. By applying it to manifest a range of knowledge concepts, we demonstrate the potential of ontology-based datafication to create coherent representations for intangible cultural entities and to enable an interoperable data infrastructure across multimodal cultural archives. [ABSTRACT FROM AUTHOR]

Background. Cardiomyopathy encompasses a broad spectrum of diseases affecting myocardial tissue, characterized clinically by abnormalities in cardiac structure, heart failure, and/or arrhythmias. Clinically heterogeneous, major types include dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RM), ischemic cardiomyopathy (ICM), among which DCM is more prevalent, while ICM exhibits higher incidence and mortality rates. Myocardial injury during cardiomyopathy progression may lead to myocardial fibrosis. Failure to intervene early and inhibit the process of myocardial fibrosis may culminate in heart failure. Cardiac fibroblasts constitute crucial cellular components determining the extent and quality of myocardial fibrosis, with various subpopulations exerting diverse roles in cardiomyopathy progression. Despite this, understanding of the cellular plasticity and transcriptional regulatory networks of cardiac fibroblasts in cardiomyopathy remains limited. Therefore, in this study, we conducted comprehensive single-cell analysis of cardiac fibroblasts in cardiomyopathy to explore differences in cellular plasticity and transcriptional regulatory networks among fibroblast subpopulations, with the aim of providing as many useful references as possible for the diagnosis, prognosis, and treatment of cardiomyopathy. Materials and Methods. Cells with mitochondrial gene expression comprising >20% of total expressed genes were excluded. Differential expression genes (DEGs) and stemness genes within cardiac fibroblast subpopulations were subjected to Gene Ontology (GO) analysis of biological processes (BP) and AUCell analysis. Monocle software was employed to analyze the pseudo-temporal trajectory of cardiac fibroblasts in cardiomyopathy. Additionally, the Python package SCENIC was utilized to assess enrichment of transcription factors and activity of regulators within cardiac fibroblast subpopulations in cardiomyopathy. Results. Following batch effect correction, 179,927 cells were clustered into 32 clusters, designated as T_NK cells, endothelial cells, myeloid cells, fibroblasts, pericytes, SMCs, CMs, proliferating cells, EndoCs, and EPCs. Among them, 8148 fibroblasts were further subdivided into 4 subpopulations, namely C0 THBS4+ Fibroblasts, C1 LINC01133+ Fibroblasts, C2 FGF7+ Fibroblasts, and C3 AGT + Fibroblasts. Results from GO_BP and AUCell analyses suggest that C3 AGT + Fibroblasts may be associated with immune response activation, protein transport, and myocardial contractile function, correlating with disease progression in cardiomyopathy. Transcription factor enrichment analysis indicates that FOS is the most significant TF in C3 AGT + Fibroblasts, also associated with the M1 module, possibly implicated in protein hydrolysis, intracellular DNA replication, and cell proliferation. Moreover, correlation analysis of transcriptional regulatory activity between fibroblast subpopulations reveals a more pronounced heterogeneity within C3 AGT + Fibroblasts in cardiomyopathy. Conclusion. C3 AGT + Fibroblasts exhibit increased sensitivity towards adverse outcomes in cardiomyopathy, such as myocardial fibrosis and impaired cardiac contractile function, compared to other cardiac fibroblast subpopulations. The differential cellular plasticity and transcriptional regulatory activity between C3 AGT + Fibroblasts and other subgroups offer new perspectives for targeting fibroblast subpopulation activity to treat cardiomyopathy. Additionally, stemness genes EPAS1 and MYC, along with the regulator FOS, may play roles in modulating the biological processes of cardiac fibroblasts in cardiomyopathy. [ABSTRACT FROM AUTHOR]

Ontologies extraction is the cornerstone for a meaningful knowledge representation. Ontologies represent the semantic relations repository in a readable format with a clear representation of the domain knowledge. This made the automated ontologies construction a promising research objective with a direct and clear impact in many related fields, including knowledge base systems, text classification, etc. In this research, a workflow is set up for successful ontology learning from Arabic textual data. One of the bottlenecks for the text analytics field is the continuous requirement of up-to-date resources such as lexicons. This challenge is one of the main focuses of the current research, which proposes an automated ontology extraction method with no use of pre-defined resources. The research proposes a novel generic ontology learning and document classification model based on no utilization of prior text analysis resources. Moreover, a self-enrichment approach is proposed to ensure continuous knowledge construction. The research extends the ontology learning process to include the ontologies' semantic relationships, targeting a higher level of extraction and model enrichment. Two experiments have been applied with two different datasets that belong to different fields to ensure the generality of the proposed model. The results of the two experiments confirmed the high accuracy of the proposed model and its positive contribution to the classification task. The results of the ontology learning task reached 95%, while the classification task revealed the advancement of the Bagging algorithm over other machine learning algorithms with an accuracy equal to 97.92%. [ABSTRACT FROM AUTHOR]

In our societally extractive age, sport science risks being swept up in the intensifying desire to commodify the experiences of those that scientists proclaim to study. Coupled with the techno-digital revolution, this stems from a vertical (onto)logic that frames the sporting landscape as a static space filled with discrete objects waiting for us to capture, analyse, re-present and sell on as knowledge. Not only does this commodification degrade primary experience in the false hope of epistemological objectivity, it reinforces the unidirectionality of extractivism by setting inquirer apart from, and above of, inquiry. Here, we advocate for a different, more sentient logic grounded in the relationality of gifting as understood in indigenous philosophies. This foregrounds an ecological orientation to scholarship that sets out neither to objectify or describe that which is of concern, but to correspond with its becoming. On this, there are three threads we cast forward. First, in a corresponsive sport science, inhabitants are not objects of analysis, but lines in-becoming, who in answering to others, form knots in a meshwork. These knots constitute communal places in which inhabitants have joined with the differentiating coming-into-being of others. Second, knowledge is not authoritatively (re)cognitive, but humbly ecological; not produced vertically through imposition, but grown longitudinally in responsively moving from place to place. Third, research does not follow a vertically extractive (onto)logic, but is a practice of participant observation. This perspective appreciates that we, sport scientists, are also lines in-becoming that form parts of the knots in which we seek to know. In coda, our thesis is not a call for more qualitative or applied research in the sport sciences. It is a call to response-ably open up to that which sparks our curiosity, answering to what is shared with care, sensitivity and sincerity. [ABSTRACT FROM AUTHOR]

This study explores the 'sex-map', a didactic object developed in Sweden. The analysis focuses on teacher guidelines and an animated movie for classroom use and how the sex-map becomes a method for emphasizing students as actors in defining sexuality. Building on Mol's notion of ontonorms, the emphasis is on ways in which the ontology of young sexuality is associated with arguments about what is 'good' in and for sex education. The sex-map incorporates ideal students' experiences, discoveries, and positive feelings. Via students, a critique is mounted against one-path sexuality, underscoring the importance of 'good' non-hierarchical sexuality as exemplary sex education. [ABSTRACT FROM AUTHOR]

Bridges are essential portions of a nation's infrastructure systems. Although general rules and guidelines are available for bridge preservation activity prediction, due to the intricate interdependencies among bridge elements, defects, and preservation activities, departments of transportation (DOTs) rely heavily on bridge engineers' experience to determine preservation needs. Hence, identifying and organizing the unwritten and experience-based domain knowledge is essential to automate bridge preservation planning. This research collected 13,994 defects for 442 bridges in North Carolina. A graph neural network (GNN) model was developed to predict preservation activities using a defect dependency graph. This research created a bridge preservation ontology to further leverage experience-based domain knowledge to derive 80 unwritten activity-triggering rules via ontology axioms. A heterogeneous graph was constructed considering the semantics related to bridge defects and elements from the axioms. Tests revealed that, with a domain ontology, the GNN model improved prediction accuracy, precision, recall, and F1 score by 4.78%, 4.03%, 15.03%, and 11.62%, respectively. This research contributes to the body of knowledge by proposing a new graph theory–based bridge inspection database to enable GNN learning considering spatial and logical dependencies. Construction practitioners can instantly access and clearly comprehend bridge maintenance contextual information using the ontology database and machine learning models. The framework provides a systematic model for bridge preservation activity planning and enhances the robustness and reliability of bridge preservation decision-making. This research will assist DOT engineers and managers in improving knowledge sharing and automatic planning in bridge management. [ABSTRACT FROM AUTHOR]

Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions. URL: https://cardio-care.shinyapps.io/VEP%5fFinder/. Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct. [ABSTRACT FROM AUTHOR]

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO , http://pcaontology.net/ and http://pcaontology.net/mobile/. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) and geographic information system (GIS) are often integrated to achieve more refined urban management. Industry Foundation Classes (IFC) and City Geography Markup Language (CityGML) are widely used standards in BIM and GIS. The main problem of the transformation of these two standards lies in the gap between them in geometry and semantics. This article introduces an ontology‐based rule mapping approach, achieving the transformation from IFC to CityGML through coordinate system conversion, geometric expression conversion, and semantic mapping. For semantic mapping, we have designed and established rules, enabling the effective mapping of the IFC model to the CityGML level of detail 0 (LOD0) to LOD4 model based on entities, appearance properties and building properties. To fully demonstrate the entire process, we give a detailed elaboration of the conversion of several IFC models. [ABSTRACT FROM AUTHOR]

Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely "time‐topic," "region‐topic," and "map auxiliary elements" for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies. [ABSTRACT FROM AUTHOR]

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans , Drosophila , zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML). [ABSTRACT FROM AUTHOR]

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD. [ABSTRACT FROM AUTHOR]

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications. [ABSTRACT FROM AUTHOR]

Poria sini decoction (PSD), a significant traditional Chinese herbal formula, is effective in liver cancer (LC) and chronic heart failure (CHF); however, little is known about its concurrent targeting mechanism. Methods. This study analyzed the potential molecular mechanism of PSD against the two distinct diseases using network pharmacology approaches, including multidatabase search, pharmacokinetic screening, network construction analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and molecular docking to elaborate the active components, signaling pathways, and potential mechanisms of PSD in the treatment of both LC and CHF. Results. A total of 155 active components and 193 potential targets in PSD were identified. Bioinformatics analysis revealed that quercetin, isorhamnetin, and naringenin, etc. may be potential candidate agents. TNF, AKT1, and IL6, etc. could become potential therapeutic targets. TNF-α, NF-κB, PI3K-AKT, and TRP signaling pathways might play an important role in PSD against LC and CHF. Molecular docking results showed that most screened active compounds could embed itself into target proteins with a high binding affinity, and the hydrogen bonds number ≥3 indicated a more stable conformation of the compounds and target proteins. Overall, quercetin and isorhamnetin were the main active components, and TNF and AKT1 were the primary targets for PSD treatment of LC and CHF. Conclusions. This study illustrated that quercetin contained in PSD played an important role in the treatment of LC and CHF by acting on the key gene of TP53 and downregulating the PI3K-AKT signaling pathway. [ABSTRACT FROM AUTHOR]

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

Background: A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. Methods: Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. Results: A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. Conclusions: In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation. [ABSTRACT FROM AUTHOR]

Youth Participatory Action Research (YPAR) has often been characterized as a meaningful way of including young people in research about and for them. Much has been written about the need to develop trusting relationships between adult and youth researchers in this process. These types of research relationships take time to develop authentically and we see a need for that relationship-building time to be built into research designs and timelines. At the same time, in our experience working with the Youth Research Council in Northern Virginia, USA, we have seen youth researchers express desires to use "rapid" research methods, such as hallway interviews, text messaging, speak-back surveys, and video testimonials to gather data from peers. The purpose of this article is to explore these tensions, to uncover the privilege of what have been called slow ontologies, and to offer recommendations for other youth-led research teams. [ABSTRACT FROM AUTHOR]

The concept of dignity is not, as some scholars claim, an unnecessary moral idea, and nor need it have religious overtones or be characterised by speciesism. In this article, I try to show that dignity can be defined and recognised. The starting point for the argumentation is the four typologies of dignity, which show that the term 'dignity' can denote significantly different concepts, and that the different concepts of dignity can have significantly different ontological senses. A unified typology of dignity allows for five categories to be distinguished: inherent dignity, dignity based on changeable qualities, moral dignity, bestowed dignity and comportment dignity. I take the first two categories of dignity as the object of the analysis, with which I seek to formulate a philosophical response to the charge of speciesism and to show on what basis it can be maintained that all human beings possess dignity. To this end, I distinguish between existential dignity, actual dignity, and potential dignity. Distinguishing these types of dignity becomes possible in the light of Aquinas' and Aristotle's views. In the final section, I point to two ways of recognising dignity. The first is based on certain narratives and emotional states ('ecumenical model of dignity'), while the second is related to a specific moral experience developed within ethical personalism. [ABSTRACT FROM AUTHOR]

Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer's patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent "TimeOntoImperfection", a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of "TimeOntoImperfection", then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step. [ABSTRACT FROM AUTHOR]

Background: Molecular-based approaches to understanding concussion pathophysiology provide complex biological information that can advance concussion research and identify potential diagnostic and/or prognostic biomarkers of injury. Objective: The aim of this study was to identify gene expression changes in peripheral blood that are initiated following concussion and are relevant to concussion response and recovery. Methods: We analyzed whole blood transcriptomes in a large cohort of concussed and control collegiate athletes who were participating in the multicenter prospective cohort Concussion Assessment, Research, and Education (CARE) Consortium study. Blood samples were collected from collegiate athletes at preseason (baseline), within 6 h of concussion injury, and at four additional prescribed time points spanning 24 h to 6 months post-injury. RNA sequencing was performed on samples from 230 concussed, 130 contact control, and 102 non-contact control athletes. Differential gene expression and deconvolution analysis were performed at each time point relative to baseline. Results: Cytokine and immune response signaling pathways were activated immediately after concussion, but at later time points these pathways appeared to be suppressed relative to the contact control group. We also found that the proportion of neutrophils increased and natural killer cells decreased in the blood following concussion. Conclusions: Transcriptome signatures in the blood reflect the known pathophysiology of concussion and may be useful for defining the immediate biological response and the time course for recovery. In addition, the identified immune response pathways and changes in immune cell type proportions following a concussion may inform future treatment strategies. [ABSTRACT FROM AUTHOR]

The article discusses various approaches to prevention-oriented practices in counseling, emphasizing the importance of empowerment, cultural wealth, and community interconnectedness in addressing health disparities in Black and Latine families.Topics include empowerment-based prevention models, the unique position of school counselors, and the application of time series analysis in research.

This paper argues that the philosophy of ambiguity, associated with Maurice Merleau-Ponty and Simone de Beauvoir, provides a helpful framework for understanding the complex and paradoxical nature of old age outside the dominant categories of the "third" and "fourth" ages. Building on philosophical literature in cultural gerontology including that focused on the "art of living" and other approaches that draw on existentialist thought, it suggests "ambiguity" as a theme that has been overlooked in such literature. The advantage of this approach is that it can accommodate contradictory elements and thus underpin an integrated approach to old age. At the same time, in distinguishing between ontological and social dimensions of ambiguity, the conceptual framework offers a new perspective on ageism that can help clarify the distinction between ageism as oppression and the negative aspects of deep old age itself. The paper is structured as follows. After making the case for the value and importance of "ambiguity" as a framework for viewing old age, I set out the key aspects of this philosophy as found in the work of Merleau-Ponty and particularly Beauvoir. I then apply this framework to a discussion of 2 literary memoirs of deep old age, by Diana Athill and Florida Scott-Maxwell, respectively. I argue that the picture of old age that emerges contrasts with the hegemonic discourses enframed by successful aging and frailed old age and offers the possibility not only of a true appreciation of old age but also of a more meaningful life course itself. [ABSTRACT FROM AUTHOR]

Climate change has led to an increase in the frequency and intensity of natural disasters, necessitating the development of efficient crisis management strategies for population sheltering. However, existing research on this topic primarily focuses on the use of public resources such as ambulances and fire trucks, which may sometimes be insufficient due to high demand and impacted locations, worsening the shortage of resources. This research introduces an ontology-based crisis simulation system for population sheltering management that focuses on the integration and distribution of citizen–volunteer drivers/vehicles into the evacuation process. Recognizing the limitations of public resources in current crisis management models, our approach incorporates citizen resources to enhance overall evacuation capacity. We develop an ontology to standardize crisis management knowledge, frame vehicle distribution as a recommendation problem, and design a simulation module incorporating a constraint-based recommender system. The proposed scenario illustrates how the simulation system can recommend citizen resources during crisis situations by considering the constraints to be satisfied. With our system, we aim at helping stakeholders to be prepared for various disaster scenarios: optimizing resource allocation and reducing time to make decisions by decision-makers. [ABSTRACT FROM AUTHOR]

Risk management which assesses and mitigates risks such as suicide and violence is under scrutiny, particularly within psychiatric inpatient settings. Restrictive practices, which result from risk assessment, such as observations, physical restraint and ward seclusion can impact negatively on patient recovery, hindering abilities to develop a meaningful life that emphasizes purpose, hope and autonomy, despite experiencing mental distress. Yet, less is known about the impact from the patient's perspective when first admitted to hospital, a period which among other reasons may come with increasing risk management practices owing to the clinical uncertainties about patient risks. In this grounded theory study, we explore the impact on recovery, interviewing 15 adult participants with patient experiences of being in an acute hospital. The main theme of the study, termed a core category with a grounded theory, was identified as "ontological insecurity of inattentiveness". This highlighted a staff inattentiveness with involving patients with risk management and explaining the purposes of the practice, which raised insecurities about what was happening to the patients when admitted to hospital. Four subcategories support the core category; discounting the patients' experiences to gain a meaningful grasp of risk management, ambiguity about risk management rules, particularly the reasons around their use, forebodingness to the hospital environment and, management from afar, with patients feeling scrutinized from observations without a voice to offer different views. It is hoped these findings will add to the field of patient involvement in psychiatric inpatient settings, proposing attempts to raise understanding and inclusivity of risk management, starting when first admitted to hospital. [ABSTRACT FROM AUTHOR]

Temporal information plays a crucial role in historical research, as it enables scholars to gain insights into the events and processes that have shaped the past. However, the complexity and diversity of temporal descriptions found in Chinese historical texts pose significant challenges for analyzing and interpreting this information. This article addresses these challenges by introducing the traditional Chinese time ontology (TCT Ontology), which integrates relevant concepts and different timing methods into an ontology. The TCT Ontology comprises four classes, including the TCT Record class, Chinese Calendar class, Historical Interval class, and Person class, to represent time descriptions in Chinese texts. By separating time records and the traditional Chinese calendar, the ontology provides a reference model for understanding time information in Chinese historical archives and serves as a basis for converting those time records to the Gregorian calendar. This accurate conversion is critical for humanistic research in Chinese history, as it enables scholars to engage in meaningful reading, studying, and research of the historical record. [ABSTRACT FROM AUTHOR]

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain. [ABSTRACT FROM AUTHOR]

Faced with the challenges of intelligently managing the entire product lifecycle, traditional management techniques no longer meet the complex demands of modern enterprises. To address it, we employ an ontology modeling method based on a multidimensional view of product information for a knowledge graph, culminating in a comprehensive PLM knowledge system. To better serve PLM users, we propose an intelligent recommendation model based on context and user behavior for knowledge recommendations. This model seamlessly integrates the knowledge storage structure, user preference attributes, and the rich semantics of the knowledge graph. To improve the recommendation capability of the model, we design an improved multi-hop neighbor node sampling algorithm and use a graph convolutional neural network to quantify the target user preference attributes layer by layer. After a series of rigorous validation experiments, our method consistently outperformed competing models across key performance metrics, such as accuracy and recall, underscoring its efficacy and practicality in knowledge recommendation within PLM systems. [ABSTRACT FROM AUTHOR]

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenario-based ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is one of the most common cancers among women. Since microarray is a new method in cancer diagnosis, in the current study, the microarray library of MCF-7 breast cancer cells after irradiation was examined to predict the gene expression pattern and metabolic pathways related to it. Materials and Methods: Microarray data extracted from National Center for Biotechnology Information (NCBI) related to MCF-7 cells were analyzed by GEO2R and R software. Then the genes that had increased and decreased expression were extracted and gene ontology analysis was performed using DAVID and Enrichr databases. Results: In this study, after 48 hours of irradiation, 234 genes with Log2FC>1 increased expression and 146 genes with Log2FC<-1 decreased expression (Adjusted p value <0.05). KEGG analysis on genes with increased expression showed that these genes are related to type 1 diabetes (P=2.43E-04). The genes extracted in this pathway included HLA-DRB5, HLA-DRB4, CPE, TNF, HLA-DRB1 and HLA-DM (Adjusted p value <0.05). Also, the findings show that the genes involved in type 1 diabetes in the islets of Langerhans include CPE, MHC-II, and TNF-α. Conclusion: The findings of this study showed that the increase in irradiation time in breast cancer cells probably leads to the activation of pathways related to type 1 diabetes through CPE, MHC-II and TNF-α genes in the islets of Langerhans. [ABSTRACT FROM AUTHOR]

Rather than confining the categories health and sickness to a biomedical conception of the biological organism, there is growing recognition of epistemological and ontological multiplicity in the realm of diagnosis and, indeed, in the very realm of disease itself. In short, the empirical manifestations of health and illness as well as the processes thought to cause them are now understood to assume a much wider variety of both biological and other forms. This essay considers the underlying epistemological and ontological opportunities and challenges of taking what we are calling this diffusion of diagnosis seriously. By diffusion we mean the movement from a concentrated understanding of diagnostic authority as confined to scientific biomedicine to a less concentrated appreciation of the diverse approaches to diagnosis throughout the world. We consider the extent to which, and the manner in which, we as sociologists of diagnosis might not only critique these various processes but perhaps also take them seriously in an ethnographic sense as locally produced, evaluated and legitimated forms of health care. [ABSTRACT FROM AUTHOR]

Background and aim: The aim of the present article was to elaborate on a research approach and method called 'lifeworld hermeneutics'. Significant to lifeworld hermeneutics is that interpretation is the main methodological instrument for explaining and understanding existential research questions and lived experiences. From a caring science perspective, this often refers to research that aims to gain a deeper understanding of existential phenomena and issues, such as existential meaning of health, well‐being, homelessness, lostness, suffering and ageing, as well as what it means to experience unhealthiness and illness, the need for care, and caring that responds to such needs. Design: Theoretical paper. Result: The article briefly covers ontology and epistemology that clarifies the meaning and importance of a lifeworld hermeneutic attitude. This is followed by suggestions for how to perform a lifeworld hermeneutic study, expressed in relation to methodological principles for the interpretation, validation and structuring of interpretations. Thereafter, follow reflections on how to use theoretical or philosophical support to develop and deepen existential interpretations. The findings of lifeworld hermeneutic research consist of existential interpretations where the researcher, with an open and pliable attitude towards the phenomenon and the aim of the study, clarifies, explains and suggests new ways of understanding participants' lived experiences; the researcher should maintain such an attitude towards their understanding of the phenomenon as well. Conclusion: The lifeworld hermeneutical approach and method described in this article makes it possible to further deepen the understanding and knowledge about existential issues that is relevant for caring and caring science. [ABSTRACT FROM AUTHOR]

Imaginaries arerepresentational assemblages of the past, ways to understand and(re)create history and projections of the self and others that ground ever-changing identities. They are embedded in the cultural meaning-making systems of each society. However, researchers and practitioners within the heritage field have not directly analysed heritage as a product-producer of imaginaries or conceived heritage itself as an imaginary. This conceptual article proposes imaginaries as a useful analytical lens to critically study heritage. Imaginariesenable us to uncover how people assume and signify heritage from various positions and experiences. Furthermore, this article aims to shed light on how alternative imaginaries grounded in non-western ontologies enable us to rethink heritage meaning and practice in the encounters and conflicts between different systems of meaning in daily life. More concretely, we identify three significant contributions of using imaginaries as a lens for the study of heritage. To illustrate our theoretical propositions, we incorporate empirical examples from ethnographic fieldwork conducted in the Ecuadorian Andes. [ABSTRACT FROM AUTHOR]

We use ontology to capture the knowledge about domains. There are considerable number of ontologies which have been developed. To develop a new ontology, we use the existing concepts of the ontologies according to the domain needs. The reusability of the concept maintains shareability of knowledge across the domains and prevents multiple interpretation of the defined concept. An Internationalized Resource Identifier (IRI) of the concept is used to connect the various concepts with each other. This work aims to provide an IRI-Debug tool that enables the ontologist to validate their crafted ontology against the standard ontologies using IRI and gauging to what extent their ontology's concepts or properties, if reused, compliant to the standard ontologies. This tool allows user to select a desired appropriate ontology from the available ontologies and validate the developed ontology with respect to standard ontologies. [ABSTRACT FROM AUTHOR]

Ontologies are domain-specific metadata that describe relationships between a specific field's properties, sample data of this field, and properties developed for many different purposes. Also, ontologies can be defined by other names within the same domain. Ontology matching algorithms eliminate definition differences and find similarities between existing ontologies. Ontology matching algorithms are used especially for information management, data integration, information extraction, etc. In this study, a graph-based framework is proposed to match large ontologies. This framework is aimed to divide the large ontologies into small pieces and then matches them using sub-graph mining algorithms. Karger algorithm and CP (clique percolation and nearest neighbor) algorithm are used to divide large ontologies. Both algorithms were applied to ontologies for the first time. In the next step, these obtained sub-parts are matched by using sub-graph mining algorithms. GraMi and gSpan algorithms were selected and were used for the first time in the field of ontology matching. We validated our framework using Anatomy and Conference data sets. Also, the proposed framework is compared with widely used in the literature AML and Falcon-AO matching algorithms. According to obtained the results, it is seen that GraMi is better than matching algorithms. [ABSTRACT FROM AUTHOR]

While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon‐based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon‐based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon‐based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon‐based classification systems. [ABSTRACT FROM AUTHOR]

Couple therapists have the unique and challenging opportunity of helping people find deeper connection in their intimate relationships. These clinicians apply therapeutic models and interventions designed to help couples. However, many of these models are derived from theoretical, scientific, and sociocultural traditions that conceptualize human phenomena as individualistic and reductionistic, and the language in these theories may not match clients' experiences, which include deeply relational phenomena such as love and loss. We review how Western scientific and sociocultural traditions have shaped conceptualization of problems and clinical intervention in couple therapy. In contrast, we provide an alternative, strongly relational framework for couple therapy that draws upon philosophical perspectives asserting that relational, rather than individual, experience is fundamental in human interaction. These ideas provide relational language which can influence how couple therapy is viewed and enacted. Specifically, we discuss how a strongly relational approach changes how therapists understand and intervene with conflict, abuse, love, and deception. This relational framework can help couples in their own goals to become more unified. Implications for the therapeutic relationship, interventions, and scholarship are provided. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer is a leading cause of cancer-related deaths worldwide, characterized as a disease usually diagnosed in the advanced stages with limited therapeutic options. Significant progress in the treatment of lung cancer has been achieved by the application of targeted therapy and immunotherapy based on the identification of molecular abnormalities of the tumor tissue. However, only a small proportion of patients is carrying genetic alterations and will benefit from targeted therapy. Here, we conducted transcriptomic profiling and a comprehensive biostatistics analysis of the squamous-cell lung carcinoma (SqCLC), a histological subtype of the non-small cell lung carcinoma (NSCLC), aiming to identify the specific transcriptomic signature of SqCLC and evaluate the functional relevance of identified genes. This study sheds light on individual SqCLC tumors' transcriptomic landscape and discusses the therapeutic and prognostic potential of identified biomarkers. Squamous cell lung carcinoma (SqCLC) is associated with high mortality and limited treatment options. Identification of therapeutic targets and prognostic biomarkers is still lacking. This research aims to analyze the transcriptomic profile of SqCLC samples and identify the key genes associated with tumorigenesis, overall survival (OS), and a profile of the tumor-infiltrating immune cells. Differential gene expression analysis, pathway enrichment analysis, and Gene Ontology analysis on RNA-seq data obtained from FFPE tumor samples (N = 23) and healthy tissues (N = 3) were performed (experimental cohort). Validation of the results was conducted on publicly available gene expression data using TCGA LUSC (N = 225) and GTEx healthy donors' cohorts (N = 288). We identified 1133 upregulated and 644 downregulated genes, common for both cohorts. The most prominent upregulated genes were involved in cell cycle and proliferation regulation pathways (MAGEA9B, MAGED4, KRT, MMT11/13), while downregulated genes predominately belonged to immune-related pathways (DEFA1B, DEFA1, DEFA3). Results of the survival analysis, conducted on the validation cohort and commonly deregulated genes, indicated that overexpression of HOXC4 (p < 0.001), LLGL1 (p = 0.0015), and SLC4A3 (p = 0.0034) is associated with worse OS in early-stage SqCLC patients. In contrast, overexpression of GSTZ1 (p = 0.0029) and LILRA5 (p = 0.0086) was protective, i.e., associated with better OS. By applying a single-sample gene-set enrichment analysis (ssGSEA), we identified four distinct immune subtypes. Immune cell distribution suggests that the memory T cells (central and effector) and follicular helper T cells could serve as important stratification parameters. [ABSTRACT FROM AUTHOR]

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat. [ABSTRACT FROM AUTHOR]