Online Mendelian Inheritance in Animals (OMIA) is a freely available curated knowledgebase that contains information and facilitates research on inherited traits and diseases in animals. For the past 29 years, OMIA has been used by animal geneticists, breeders, and veterinarians worldwide as a definitive source of information. Recent increases in curation capacity and funding for software engineering support have resulted in software upgrades and commencement of several initiatives, which include the enhancement of variant information and links to human data resources, and the introduction of ontology-based breed information and categories. We provide an overview of current information and recent enhancements to OMIA and discuss how we are expanding the integration of OMIA into other resources and databases via the use of ontologies and the adaptation of tools used in human genetics.

Numerical cognition is a field that investigates the sociocultural, developmental, cognitive, and biological aspects of mathematical abilities. Recent findings in cognitive neuroscience suggest that cognitive skills are facilitated by distributed, transient, and dynamic networks in the brain, rather than isolated functional modules. Further, research on the bodily and evolutionary bases of cognition reveals that our cognitive skills harness capacities originally evolved for action and that cognition is best understood in conjunction with perceptuomotor capacities. Despite these insights, neural models of numerical cognition struggle to capture the relation between mathematical skills and perceptuomotor systems. One front to addressing this issue is to identify building block sensorimotor processes (BBPs) in the brain that support numerical skills and develop a new ontology connecting the sensorimotor system with mathematical cognition. BBPs here are identified as sensorimotor functions, associated with distributed networks in the brain, and are consistently identified as supporting different cognitive abilities. BBPs can be identified with new approaches to neuroimaging; by examining an array of sensorimotor and cognitive tasks in experimental designs, employing data-driven informatics approaches to identify sensorimotor networks supporting cognitive processes, and interpreting the results considering the evolutionary and bodily foundations of mathematical abilities. New empirical insights on the BBPs can eventually lead to a revamped embodied cognitive ontology in numerical cognition. Among other mathematical skills, numerical magnitude processing and its sensorimotor origins are discussed to substantiate the arguments presented. Additionally, an fMRI study design is provided to illustrate the application of the arguments presented in empirical research.

BACKGROUND: In the European Union, rare diseases are defined as diseases that affect maximum 5 in 10,000 citizens. These diseases are typically associated with a high unmet medical need. To stimulate development and authorisation of medicines for rare diseases (\'orphan conditions\'), the European Commission (EC) can grant orphan designations. In order to enable systematic evaluation and communication of the diseases for which designated orphan medicines have (not) been developed and authorised, we aimed to investigate the feasibility of important disease terminology systems for mapping orphan conditions and therapeutic indications.
METHODS: We selected all designated orphan medicines that were authorised by the EC during 2022-2023 from the EC\'s Union Register of medicinal products. For these medicines, we extracted orphan conditions and associated therapeutic indications at initial marketing authorisation. The orphan conditions and separate elements of therapeutic indications such as target disease or condition, severity criteria and target population were assessed for availability in six major disease terminology systems: ICD-10, ICD-11, MedDRA, MeSH, Orphanet nomenclature of rare diseases, and SNOMED CT. Descriptive statistics were used to describe the ability of each disease terminology system to map orphan conditions and elements of therapeutic indications.
RESULTS: During 2022-2023, 37 designated orphan medicines were authorised that were designated for 40 orphan conditions (of which 37 unique) and granted 39 therapeutic indications (of which 37 unique). Overall, SNOMED CT covered most descriptions of orphan conditions (33/37, 89%) and target diseases or conditions within therapeutic indications (28/37, 76%). However, when allowing descriptions to be partly included and/or complemented by additional words, SNOMED CT, the Orphanet nomenclature, ICD-11 and MedDRA all had high coverage (92-97%). Other elements than target diseases or conditions within therapeutic indications were mostly lacking.
CONCLUSIONS: Regulatory data concerning orphan conditions and therapeutic indications of designated orphan medicines seem to be best covered by SNOMED CT. However, which disease terminology system best facilitates systematic evaluation and communication about development and authorisation of designated orphan medicines also dependents on the specific use case. Given the frequent use of SNOMED CT in healthcare settings, it may also facilitate interoperability between regulatory and healthcare data, while for example ICD-11 may be better suited to generate statistics concerning drug development for rare diseases.

BACKGROUND: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool.
METHODS: We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology\'s creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool\'s functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool\'s productive utilization.
RESULTS: The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool\'s evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise.
CONCLUSIONS: Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it\'s crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

BACKGROUND: Identifying chemical mentions within the Alzheimer\'s and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection.
RESULTS: There were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For the CRAFT corpus, our method outperformed baselines (maximum 78.4%) with a 91.17% accuracy. For our Alzheimer\'s and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal.
CONCLUSIONS: Use of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer\'s and dementia.

The tradition of natural kinds has shaped philosophical debates about scientific classification but has come under growing criticism. Responding to this criticism, Reydon and Ereshefsky present their grounded functionality account as a strategy for updating and defending the tradition of natural kinds. This article argues that grounded functionality does indeed provide a fruitful philosophical approach to scientific classification but does not convince as a general theory of natural kinds. Instead, the strengths and limitations of Reydon and Ereshefsky\'s account illustrate why it is time to move beyond general definitions of \"natural kind\" and experiment with new philosophical frameworks.

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer\'s Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

Online Mendelian Inheritance in Animals (OMIA is a freely available information resource, which includes information for Equus inherited traits/diseases (collectively called phenes). The database focuses on Mendelian traits and their likely causal variants (mutations). Some of these Mendelian traits are favored by humans, e.g., coat color, while others are diseases. Additions to OMIA are based on publications of peer-reviewed research. Maintaining up-to-date information in OMIA is a challenge, owing to the multiplicity of species, the increase in the number of relevant publications, and as reference genomes and methods of citation continue to evolve. This challenge has been successfully aided by contributions from scientists from around the world. In some cases, those scientists are faculty members who charge their students with curation as an educational activity. Recently, OMIA has introduced computerized lists of standardized names and synonyms (called ontologies) for breeds of Equus and other animals and for phene categories. These ontologies facilitate increased connectivity between OMIA and other online resources. OMIA is and will continue to be a major reference resource for Mendelian phenes in the genus Equus.

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems.

EHR Interoperability is crucial to obtain a set of benefits. This can be achieved by using data standards, like ontologies. The Portuguese Nursing Ontology (NursingOntos) is a reference model describing a set of nursing concepts and their relationships, to represent nursing knowledge in the Electronic Health Records (EHR). The purpose of this work was to define a set of correspondences between Nursing Ontology concepts of NursingOntos and other terminologies, which have the same or similar meaning. In this project, we are using the ISO/TR12300:2016 standard on the principles of mapping between terminological systems. Regarding the domain of \"airway clearance\", we can say that Portuguese Nursing Ontology has a good level of mapping with other terminologies. In conclusion, we can say that Portuguese Nursing Ontology can be used in EHR with the purpose of a global digitalization of health.