Search

To develop an ultrasound (US)-guided ventral approach to the brachial plexus (BP) and evaluate nerve anatomy and staining in barred owl cadavers. Prospective, cadaveric study. Eleven adult male and female barred owl cadavers with a body mass of 0.43–0.98 kg. Eleven frozen cadavers were thawed for 48 hours, weighed and assigned a body condition score. Ten cadavers were placed in dorsal recumbency with wings abducted. US-guided visualization of the BP was achieved by placing a 13-6 MHz linear probe over the ventral aspect of the scapulohumeral joint, parallel to the sternum. A 22 gauge, 50 mm insulated needle was advanced in-plane in a caudal-to-cranial direction. In each owl, injection targeting one BP was performed with 0.4 mL kg–1 of a 1:1 0.5% ropivacaine and 1% methylene blue solution. Dissection was performed 15 minutes postinjection. Nerve staining was deemed successful if ≥ 1 cm of circumferential staining was achieved. The eleventh owl cadaver was injected with a 1:1 solution of 1% methylene blue and 74% ioversol contrast into both wings, and computed tomography (CT) was performed just before and 15 minutes after injection. The BP was clearly identified ultrasonographically in cadavers weighing > 0.5 kg. An injectate volume of 0.4 mL kg–1 provided complete staining of the BP branches in all cadavers. CT scan revealed no contrast within the coelomic cavity. The US-guided BP injection using a ventral approach was easily performed in barred owl cadavers weighing > 0.5 kg. The injection of 0.4 mL kg–1 of a ropivacaine–dye solution resulted in complete staining of the BP branches in all wings, suggesting that this technique could provide analgesia for structures distal to the scapulohumeral joint. Clinical studies are necessary to confirm the safety and efficacy of this technique in barred owls and other bird species. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding a species' distribution is vital for making sound conservation and management decisions. Secretive behaviors and complicated seasonal movement patterns have made range delimitation surprisingly elusive for some North American birds, including the Northern Saw-whet Owl (Aegolius acadicus). Despite being among the most common of North American forest owls, only the species' breeding range is well demarcated. This has resulted in the species historically being considered a "northern" bird, with eastern migratory and nonbreeding ranges extending only as far south as central Missouri and eastern Tennessee. Recent banding efforts have shown that saw-whet owls are fall migrants much farther south. We extend this work by investigating stopover duration and nonbreeding habitat selection during 2 winters at the edge of the species' southern distribution in the Ozark Highlands of northwest Arkansas. We used radio telemetry to track 27 owls, with 17 individuals detected at least once post-release (n = 54 surveys and 131 total detections). Length of stay in the study area was most influenced by year—birds remained an average of 3.6 weeks longer during an irruption year—and our data suggest that the species uses the Ozark Highlands for both nonbreeding residency and brief stopover periods. During the 2 year study, 37% of marked owls were winter residents, 26% remained in the area temporarily, and 37% were not redetected. Winter habitat preferences of saw-whet owls in the southeast are consistent with what is known about the species in its more northernly range, with birds selecting conifers on south-facing slopes in areas with an open understory. Overall, our results suggest that saw-whet owls are not rare fall migrants and winter residents in Arkansas as was previously assumed, and more generally, are likely underappreciated winter predators in southern pine ecosystems. [ABSTRACT FROM AUTHOR]

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto , a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI). [ABSTRACT FROM AUTHOR]

Electronic commerce and finance are progressively supporting and including decentralized, shared and public ledgers such as the blockchain. This is reshaping traditional commercial activities by advancing them towards Decentralized Finance (DeFi) and Commerce 3.0, thereby supporting the latter's potential to outpace the hurdles of central authority controllers and lawgivers. The quantity and entropy of the information that must be sought and managed to become active participants in such a relentlessly evolving scenario are increasing at a steady pace. For example, that information comprises asset or service description, general rules of the game, and specific technologies involved for decentralization. Moreover, the relevant information ought to be shared among innumerable and heterogeneous stakeholders, such as producers, buyers, digital identity providers, valuation services, and shipment services, to just name a few. A clear semantic representation of such a complex and multifaceted blockchain-based e-Commerce ecosystem would contribute dramatically to make it more usable, namely more automatically accessible to virtually anyone wanting to play the role of a stakeholder, thereby reducing programmers' effort. However, we feel that reaching that goal still requires substantial effort in the tailoring of Semantic Web technologies, hence this article sets out on such a route and advances a stack of OWL 2 ontologies for the semantic description of decentralized e-commerce. The stack includes a number of relevant features, ranging from the applicable stakeholders through the supply chain of the offerings for an asset, up to the Ethereum blockchain, its tokens and smart contracts. Ontologies are defined by taking a behaviouristic approach to represent the various participants as agents in terms of their actions, inspired by the Theory of Agents and the related mentalistic notions. The stack is validated through appropriate metrics and SPARQL queries implementing suitable competency questions, then demonstrated through the representation of a real world use case, namely, the iExec marketplace. [ABSTRACT FROM AUTHOR]

Abstract Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services ("OASIS") towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat. [ABSTRACT FROM AUTHOR]

The main goal of governments is to guarantee that every individual worldwide, regardless of disability, have access to education. However, learners with disabilities exhibit higher rates of school and college dropouts than regular students. The main goal of contemporary research is to anticipate such drop-outs slightly earlier and offer recommendations and alternative pathways for their career enhancement by utilizing a novel approach LSOWL–CNN method. Initially, the dataset is pre-processed to increase the classification performance. Following that, the features are extracted and by employing ENT-QDA, the features are reduced. Rules based on fuzzy data will be created based on the features. A classification method called Linear Scaling Owl Optimal control Method with CNN (LSOWL–CNN) is employed to train the system using the created rules and testing the data the final results are classified. Results of the experiments showed that the suggested model attained an Accuracy of 96.817% which outperforms the existing models which is implementing in a python tool. [ABSTRACT FROM AUTHOR]

The performance characteristics and certain maintenance aspects of a natural language querying (NLQ) interface depend on how the data model is conceptualized. One of the areas where alternative conceptualizations are available is events and their specialization. The concept of event specialization is already known from event extraction methods, which allows for a more precise description of the events identified in a text. In the context of NLQ interfaces, event specialization allows narrower or broader questions. This study investigates how the choice of event specialization strategy in OWL (Web Ontology Language) ontologies affects the complexity and performance-related aspects of the NLQ interface to ontologies. In this paper, we present four event specialization strategies and investigate how they impact the size of the ontology schema and vocabulary of the NLQ interface, the performance of querying and data import, the size of the semantic repository, and the complexity of SPARQL queries. We discuss the strengths and weaknesses of each approach and present recommendations on determining the best one for the needs of NLQ interface end-users and developers.

The Semantic Web emerged in response to the unprecedented growth of information and data sharing on the Web. It consists of a set of technologies that enable the automatic (machine) management and processing of linked data across hundreds of distributed repositories. To connect and interlink data, the Semantic Web uses Resource Description Framework (RDF), which is a graph-based data model that simplifies the description of resources using triples (subject, predicate, object). The representation of data in RDF usually follows an ontology, a knowledge base model that dictates the relationships and characteristics of the linked data. Ontologies play an important role in the Semantic Web and are a key component. However, ontologies might not be correct and, in some cases, might not be available. In general, ontologies are created manually by domain experts in collaboration with ontology engineers, which is a costly and error-prone task. In this study, we present a proposal to automatically generate ontologies from RDF datasets. We use summarization techniques to reduce triples and retain the most relevant ones. Subsequently, classes, datatype properties, object properties, as well as the domain and range of properties are identified for schema construction. In addition, an enrichment of the schema is performed by incorporating Object Property axioms. The result is the delivery of a serialized ontology document in OWL/XML format. Furthermore, we present an experimental evaluation of an RDF dataset of 16005 triples. Through application of our summarization technique the original dataset was decreased by 98%. The ontology was generated with a time of 148.73 seconds. Finally, 9 classes, 10 Object Properties, 6 Datatype Properties, and 4 different types of Object Property axioms were identified.

Background & objectives: Mosquito vectors are disease-causing insects, responsible for various life-threatening vector-borne diseases such as dengue, Zika, malaria, chikungunya, and lymphatic filariasis. In practice, synthetic insecticides are used to control the mosquito vector, but, the continuous usage of synthetic insecticides is toxic to human health resulting in communicable diseases. Non-toxic biocontrol agents such as bacteria, fungus, plants, and mosquito densoviruses play a vital role in controlling mosquitoes. Community awareness of mosquito biocontrol agents is required to control vector-borne diseases. Mosquito vector-based ontology facilitates mosquito biocontrol by providing information such as species names, pathogen-associated diseases, and biological controlling agents. It helps to explore the associations among the mosquitoes and their biocontrol agents in the form of rules. The Mosquito vector-based Biocontrol Ontology Recommendation System (MBORS) provides the knowledge on mosquito-associated biocontrol agents to control the vector at the early stage of the mosquitoes such as eggs, larvae, pupae, and adults. This paper proposes MBORS for the prevention and effective control of vector-borne diseases. The Mosquito Vector Association ontology (MVAont) suggests the appropriate mosquito vector biocontrol agents (MosqVecRS) for related diseases. Methods: Natural Language Processing and Data mining are employed to develop the MBORS. While Tokenization, Part-of-speech Tagging (POS), Named Entity Recognition (NER), and rule-based text mining techniques are used to identify the mosquito ontology concepts, the data mining apriori algorithm is used to predict the associations among them. Results: The outcome of the MBORS results in MVAont as Web Ontology Language (OWL) representation and MosqVecRS as an Android application. The developed ontology and recommendation system are freely available on the web portal. Interpretation & conclusion: The MVAont predicts harmless biocontrol agents which help to diminish the rate of vector-borne diseases. On the other hand, the MosqVecRS system raises awareness of vectors and vector-borne diseases by recommending suitable biocontrol agents to the vector control community and researchers.

Detecting software requirements defects is crucial in reducing the risk of software project failures. Existing methods for automatic detection, especially during requirements changes, are limited in coverage and often lack robust tool support. Addressing this gap, we define the four most common types of requirements defects (incompleteness, inconsistency, redundancy, and ambiguity) and present algorithms for their detection. We propose a novel behaviour engineering-based approach, translating software requirements into behaviour trees and then into the Web Ontology Language (OWL). We developed ‘requirements defects identifier’, a tool that queries the OWL knowledge base to identify potential defects during requirements analysis and change. Validated on three final-year student projects, our approach demonstrated success in detecting all four defect types, offering broader coverage compared to existing tools. A real-world case study has been used to validate the applicability of the proposed approach. Our experiments demonstrate that the tool can successfully detect all four different types of requirement defects during both requirements analysis and requirements change.

The National Oceanic and Atmospheric Administration's (NOAA) National Data Buoy Center (NDBC) operates a network of over 100 marine weather observation buoys. These buoys presently use a wave observation system named the Digital Directional Wave Module (DDWM). The DDWM uses commercial motion sensors contained in a single package and processing boards that are now obsolete. Therefore, the NDBC completed an engineering effort to replace these obsolete components with a new wave observation system named Ocean Wave Linux (OWL). The OWL replaces the DDWM's nine axis motion sensor with a compatible device. The OWL produces the same observations as the DDWM but with much less power consumption and with modern COTS available components. The DDWM is contained in a single tube named WAVCEAN-16, which is a payload in the NDBC's Self-Contained Ocean Observation Payload (SCOOP). WAVCEAN-16 physically contains the Wave and Ocean (which measures sea surface temperature) modules. After extensive, successful field trials, the OWL components replaced the DDWM components. This effectively created a new tube or product. The new tube is named WAVCEAN-18. This paper focuses on the changes from WAVCEAN-16 to WAVCEAN-18 and highlights engineering analysis of field tests of the WAVCEAN-18. We also describe the NDBC's plans for operational transition from the WAVCEAN-16 to WAVCEAN-18. [ABSTRACT FROM AUTHOR]

Acoustic spatial capture-recapture (SCR) methods appear ideally suited for estimating population trends of cryptic species that are difficult to monitor. Despite evidence suggesting acoustic SCR offers greater precision over estimates obtained through distance sampling or mark-recapture approaches, it has seldom been applied to avian taxa. Here we assess the utility of acoustic SCR for estimating population density of owls, a group of birds for which population estimates are generally lacking. We conducted passive acoustic surveys for Barred Owls (Strix varia) and Great Horned Owls (Bubo virginianus) over the spring breeding seasons of 2020–2021 in a protected park in Georgia, USA. We used a combination of acoustic discrimination techniques to identify individuals, including spectrogram cross-correlation, hierarchical clustering, and visual scanning. The resulting estimates of the density of Great Horned Owl individuals (0.28 ± 0.026 [SE] per km2) were consistent with population density estimates from previous studies in other parts of the species' range. However, our methods appeared unsuitable for distinguishing Barred Owl individuals, which prevented estimation of population density for this species. We provide recommendations to improve acoustic sampling and individual discrimination of owls and suggest that acoustic SCR offers important advantages over conventional methods for monitoring some cryptic birds. Los métodos de captura-recaptura espacial (CRE) acústica parecen ser ideales para estimar las tendencias poblacionales de especies crípticas que son de difícil seguimiento. A pesar de la evidencia que sugiere que la CRE acústica ofrece mayor precisión que las estimaciones obtenidas mediante muestreo de distancia o enfoques de marcado-recaptura, ésta rara vez se ha aplicado a taxones aviares. Evaluamos la utilidad de la CRE acústica para estimar la densidad poblacional de búhos, un grupo de aves para el cual generalmente faltan estimaciones poblacionales. Realizamos estudios acústicos pasivos para Strix varia y Bubo virginianus durante las temporadas de cría en las primaveras del 2020 y 2021 en un parque protegido de Georgia, EEUU. Utilizamos una combinación de técnicas de discriminación acústica para identificar individuos, incluyendo correlación cruzada de espectrogramas, agrupacion jerárquica y escaneo visual. Las estimaciones resultantes de la densidad de individuos de B. virginianus (0.28 ± 0.026 [EE] por km2) fueron consistentes con las estimaciones de densidad poblacional obtenidas en estudios realizados previamente en otras áreas del rango de distribución de la especie. Sin embargo, nuestros métodos parecieron inadecuados para distinguir individuos de S. varia, lo que impidió la estimación de su densidad poblacional. Ofrecemos recomendaciones para mejorar el muestreo acústico y la discriminación individual de búhos, y sugerimos que la CRE acústica ofrece importantes ventajas sobre los métodos convencionales utilizados para el seguimiento de algunas aves crípticas. [Traducción del equipo editorial] [ABSTRACT FROM AUTHOR]

Information modelling for databases and object-oriented information systems avails of conceptual data modelling languages such as EER and UML Class Diagrams. Many attempts exist to add logical rigour to them, for various reasons and with disparate strengths. In this paper we aim to provide a structured overview of the many efforts. We focus on aims, approaches to the formalisation, including key dimensions of choice points, popular logics used, and the main relevant reasoning services. We close with current challenges and research directions.

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method.

Background: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs., Objective: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model., Methods: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the "Delta." Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources., Results: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts., Conclusions: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users., (©Tessa Ohlsen, Josef Ingenerf, Andrea Essenwanger, Cora Drenkhahn. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.09.2024.)

Owls, members of the avian order Strigiformes, are nocturnal birds of prey that are found worldwide except for Antarctica. Traumatized, free-ranging owls are commonly presented to veterinary hospitals and wildlife rehabilitation facilities with the goal of providing medical care and rehabilitation to enable release back into their natural habitat. Minimal guidelines exist for the release of wildlife, and whereas a need for functional vision is described in raptors, assessing and evaluating hearing is usually not mentioned. This can be problematic for nocturnal predators because hearing is the primary sense utilized by owls when hunting and navigating in their dark environment. The brainstem auditory evoked response (BAER) test is a minimally invasive, objective assessment of hearing commonly used in companion animals. To the authors' knowledge, routine or standardized BAER evaluation has not been reported in traumatized, free-ranging owls. In the following retrospective study, 31 free-ranging owls presented to the University of Georgia Veterinary Teaching Hospital for known or suspected trauma or being found in a debilitated state underwent BAER testing to assess for the presence of complete sensorineural hearing loss. Similar to assessment of hearing in companion animals, the BAER test was elicited using a broad click stimulus delivered at 85 dB nHL. In all owls, qualitative assessment and peak latency measurements of the BAER test reflected hearing ability. This study highlights the importance of hearing in nocturnal raptors, how BAER testing can aid in decision making regarding rehabilitation, and provides a foundation for further investigation of hearing loss in traumatized owls. We suggest that veterinarians working with free-ranging owls in a rehabilitation setting should consider BAER testing as part of routine diagnostic testing.

Background: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand., Objective: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses., Methods: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers., Results: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain., Conclusions: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice., (©Rebecca Z Lin, Muhammad Tuan Amith, Cynthia X Wang, John Strickley, Cui Tao. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.06.2024.)

Background: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard., Objective: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies., Methods: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard., Results: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability., Conclusions: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction., (©Fouzia Amar, Alain April, Alain Abran. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.01.2024.)