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The National Microbiome Data Collaborative (NMDC) is a multi-organizational effort to integrate microbiome data across diverse areas in environmental science. Data provided by the NMDC can then undergo advanced analysis and provide new insights into metagenomics, metatranscriptomics, metaproteomics, and metabolomics. To address these challenges, we have developed our schema using the Linked data Modeling Language (LinkML). This allows us to easily map data to existing standards and ontologies.

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources., Results: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues., Conclusions: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process., (© 2024. The Author(s).)

Background: The dichotomy between the hypo- versus hyperkinetic nature of Parkinson's disease (PD) and dystonia, respectively, is thought to be reflected in the underlying basal ganglia pathophysiology. In this study, we investigated differences in globus pallidus internus (GPi) neuronal activity, and short- and long-term plasticity of direct pathway projections., Methods: Using microelectrode recording data collected from the GPi during deep brain stimulation surgery, we compared neuronal spiketrain features between people with PD and those with dystonia, as well as correlated neuronal features with respective clinical scores. Additionally, we characterized and compared readouts of short- and long-term synaptic plasticity using measures of inhibitory evoked field potentials., Results: GPi neurons were slower, bustier, and less regular in dystonia. In PD, symptom severity positively correlated with the power of low-beta frequency spiketrain oscillations. In dystonia, symptom severity negatively correlated with firing rate and positively correlated with neuronal variability and the power of theta frequency spiketrain oscillations. Dystonia was moreover associated with less long-term plasticity and slower synaptic depression., Conclusions: We substantiated claims of hyper- versus hypofunctional GPi output in PD versus dystonia, and provided cellular-level validation of the pathological nature of theta and low-beta oscillations in respective disorders. Such circuit changes may be underlain by disease-related differences in plasticity of striato-pallidal synapses., Funding: This project was made possible with the financial support of Health Canada through the Canada Brain Research Fund, an innovative partnership between the Government of Canada (through Health Canada) and Brain Canada, and of the Azrieli Foundation (LM), as well as a grant from the Banting Research Foundation in partnership with the Dystonia Medical Research Foundation (LM)., Competing Interests: SS, KS, LS, CF, EH, AK, MH, SK, AL, AF, WH, LM No competing interests declared, (© 2023, Sumarac, Spencer, Steiner et al.)

Background: Within the Open Biological and Biomedical Ontology (OBO) Foundry, many ontologies represent the execution of a plan specification as a process in which a realizable entity that concretizes the plan specification, a "realizable concretization" (RC), is realized. This representation, which we call the "RC-account", provides a straightforward way to relate a plan specification to the entity that bears the realizable concretization and the process that realizes the realizable concretization. However, the adequacy of the RC-account has not been evaluated in the scientific literature. In this manuscript, we provide this evaluation and, thereby, give ontology developers sound reasons to use or not use the RC-account pattern., Results: Analysis of the RC-account reveals that it is not adequate for representing failed plans. If the realizable concretization is flawed in some way, it is unclear what (if any) relation holds between the realizable entity and the plan specification. If the execution (i.e., realization) of the realizable concretization fails to carry out the actions given in the plan specification, it is unclear under the RC-account how to directly relate the failed execution to the entity carrying out the instructions given in the plan specification. These issues are exacerbated in the presence of changing plans., Conclusions: We propose two solutions for representing failed plans. The first uses the Common Core Ontologies 'prescribed by' relation to connect a plan specification to the entity or process that utilizes the plan specification as a guide. The second, more complex, solution incorporates the process of creating a plan (in the sense of an intention to execute a plan specification) into the representation of executing plan specifications. We hypothesize that the first solution (i.e., use of 'prescribed by') is adequate for most situations. However, more research is needed to test this hypothesis as well as explore the other solutions presented in this manuscript., (© 2024. The Author(s).)

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

Self-organization of individuals within large collectives occurs throughout biology. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the 'effective' dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of continuous models and individual-based data in settings with biologically realistic, time-varying cell numbers. Specifically, we introduce and fit scaling parameters in continuous models to account for discrepancies that can arise from low cell numbers and localized interactions. We illustrate our approach on an example motivated by zebrafish-skin pattern formation, in which we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Streptococcus gordonii ( S. gordonii , Sg) is one of the early colonizing, supragingival commensal bacterium normally associated with oral health in human dental plaque. MicroRNAs (miRNAs) play an important role in the inflammation-mediated pathways and are involved in periodontal disease (PD) pathogenesis. PD is a polymicrobial dysbiotic immune-inflammatory disease initiated by microbes in the gingival sulcus/pockets. The objective of this study is to determine the global miRNA expression kinetics in S. gordonii DL1-infected C57BL/6J mice. All mice were randomly divided into four groups ( n = 10 mice/group; 5 males and 5 females). Bacterial infection was performed in mice at 8 weeks and 16 weeks, mice were euthanized, and tissues harvested for analysis. We analyzed differentially expressed (DE) miRNAs in the mandibles of S. gordonii -infected mice. Gingival colonization/infection by S. gordonii and alveolar bone resorption (ABR) was confirmed. All the S. gordonii -infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, and a significant increase in mandible and maxilla ABR ( p < 0.0001). miRNA profiling revealed 191 upregulated miRNAs (miR-375, miR-34b-5p) and 22 downregulated miRNAs (miR-133, miR-1224) in the mandibles of S. gordonii -infected mice at the 8-week mark. Conversely, at 16 weeks post-infection, 10 miRNAs (miR-1902, miR-203) were upregulated and 32 miRNAs (miR-1937c, miR-720) were downregulated. Two miRNAs, miR-210 and miR-423-5p, were commonly upregulated, and miR-2135 and miR-145 were commonly downregulated in both 8- and 16-week-infected mice mandibles. Furthermore, we employed five machine learning (ML) algorithms to assess how the number of miRNA copies correlates with S. gordonii infections in mice. In the ML analyses, miR-22 and miR-30c (8-week), miR-720 and miR-339-5p (16-week), and miR-720, miR-22, and miR-339-5p (combined 8- and 16-week) emerged as the most influential miRNAs.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement., Competing Interests: No conflict of interest to declare Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal., (Raïssa Meyer, Ward Appeltans, William D. Duncan, Mariya Dimitrova, Yi-Ming Gan, Thomas Stjernegaard Jeppesen, Christopher Mungall, Deborah L Paul, Pieter Provoost, Tim Robertson, Lynn Schriml, Saara Suominen, Ramona Walls, Maxime Sweetlove, Visotheary Ung, Anton Van de Putte, Elycia Wallis, John Wieczorek, Pier Luigi Buttigieg.)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors' rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear. Observations of the distribution of the extracellular matrix (ECM) component fibronectin during the migration of loosely connected neural crest cells (NCCs) lead us to hypothesize that NCC remodeling of an initially punctate ECM creates a scaffold for trailing cells, enabling them to form robust and coherent stream patterns. We evaluate this idea in a theoretical setting by developing an individual-based computational model that incorporates reciprocal interactions between NCCs and their ECM. ECM remodeling, haptotaxis, contact guidance, and cell-cell repulsion are sufficient for cells to establish streams in silico, however, additional mechanisms, such as chemotaxis, are required to consistently guide cells along the correct target corridor. Further model investigations imply that contact guidance and differential cell-cell repulsion between leader and follower cells are key contributors to robust collective cell migration by preventing stream breakage. Global sensitivity analysis and simulated gain- and loss-of-function experiments suggest that long-distance migration without jamming is most likely to occur when leading cells specialize in creating ECM fibers, and trailing cells specialize in responding to environmental cues by upregulating mechanisms such as contact guidance., Competing Interests: WM, RM, JT, MM, LD, RB, HB, PK, PM No competing interests declared, (© 2023, Martinson et al.)

Background: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery., Methods and Findings: ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS)., Conclusions: We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps., (© 2023. The Author(s).)

Background: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020., Results: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment., Conclusion: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications., (© 2022. The Author(s).)

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: https://github.com/INCATools/ontology-development-kit., (© The Author(s) 2022. Published by Oxford University Press.)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec., (© The Author(s) 2022. Published by Oxford University Press.)

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges., Competing Interests: Author DW is employed by Polyneme LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hu, Canon, Eloe-Fadrosh, Anubhav, Babinski, Corilo, Davenport, Duncan, Fagnan, Flynn, Foster, Hays, Huntemann, Jackson, Kelliher, Li, Lo, Mans, McCue, Mouncey, Mungall, Piehowski, Purvine, Smith, Varghese, Winston, Xu and Chain.)

Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge.

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/., (© The Author(s) 2021. Published by Oxford University Press.)

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Background: A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines' EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework., Results: The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice's EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance - a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies., Conclusions: This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

Background: Limited treatment options are available for oral mucositis, a common, debilitating complication of cancer therapy. We examined the association between daily delivery time of radiotherapy and the severity of oral mucositis in patients with head and neck cancer., Methods: We used electronic medical records of 190 patients with head and neck squamous cell carcinoma who completed radiotherapy, with or without concurrent chemotherapy, at Roswell Park Comprehensive Cancer Center (Buffalo, NY) between 2015 and 2017. Throughout a 7-week treatment course, patient mouth and throat soreness (MTS) was self-reported weekly using a validated oral mucositis questionnaire, with responses 0 (no) to 4 (extreme). Average treatment times from day 1 until the day before each mucositis survey were categorized into seven groups. Multivariable-adjusted marginal average scores (LSmeans) were estimated for the repeated- and maximum-MTS, using a linear-mixed model and generalized-linear model, respectively., Results: Radiation treatment time was significantly associated with oral mucositis severity using both repeated-MTS ( n = 1,156; P = 0.02) and maximum-MTS ( n = 190; P = 0.04), with consistent patterns. The severity was lowest for patients treated during 8:30 to <9:30 am (LSmeans for maximum-MTS = 2.24; SE = 0.15), increased at later treatment times and peaked at early afternoon (11:30 am to <3:00 pm, LSmeans = 2.66-2.71; SEs = 0.16/0.17), and then decreased substantially after 3 pm., Conclusions: We report a significant association between radiation treatment time and oral mucositis severity in patients with head and neck cancer., Impact: Although additional studies are needed, these data suggest a potential simple treatment time solution to limit severity of oral mucositis during radiotherapy without increasing cost., (©2020 American Association for Cancer Research.)

Objectives: The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR)., Methods: Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location., Results: Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset., Conclusion: This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

Background: Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data., Results: We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies., Conclusion: We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.

Background: Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively., Results: To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI., Conclusions: OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.

A 71-year-old woman who had been taking ibandronate for 10 years presented to an Endocrinology Department with persistent mid-thigh pain. Pelvic X-ray showed bilateral femoral cortical expansion, indicating impending atypical femoral fractures (AFFs). AFFs have been linked to long-term bisphosphonate therapy and have morbidity and mortality similar to that of hip fractures. Such fractures can be averted by regular reviews of bisphosphonate therapy and vigilance for prodromal symptoms. This patient's bisphosphonate therapy was stopped, and fractures were avoided by treatment with vitamin D and parathyroid hormone., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: We aimed to investigate the prognostic impact of duration of first-line chemotherapy administration in patients with epithelial ovarian cancer (EOC)., Methods: Chemotherapy records were abstracted from the electronic medical record. Patients with on-time completion (105 days) were compared to patients finishing early (<105 days), delays of 1-4 weeks, or >4 weeks. For 222 women with stage IIIC/IV, stage-stratified estimates of progression-free survival (PFS) and overall survival (OS) were compared. A delay sub-study was performed with outliers removed. Each week of delay was correlated with the change in PFS and OS to identify time points associated with change in outcome., Results: Most women had on-time completion of chemotherapy (23.6%) or a treatment delay of ≤4 weeks (21.8%); 21.6% of women experienced a delay longer than 4 weeks. R0 resection at initial debulking (OR = 1.99, 95%CI: 1.18-3.36, p = 0.010) and RECIST complete response (OR = 4.88, 95%CI: 2.47-10.63, p<0.001) were strongly associated with on-time completion. Patients with on-time completion and < 1 month delay had similar median survivals of 43.1 months (lower 95% CI bound 33.7 months) and 44.5 months (lower bound 37.0, p = 0.93). Women with >1 month delay had decreased median survival of 18.1 months (14.7-24.9 months), while women with short intervals survived 35.0 months (95%CI: 21.8-49.8 months). Short-term delays lead to progressively decreasing OS. This was significantly different from the on-schedule survival estimate after 6 weeks of delay., Conclusions: On-time completion of chemotherapy correlates with increased survival and higher complete response rates. Increasing delays in chemotherapy completion were associated with decreased survival., Competing Interests: The authors have declared that no competing interests exist.

Background: We hypothesise, based upon the findings from our previous trial, that the addition of co-trimoxazole to standard therapy is beneficial to patients with moderate to severe idiopathic pulmonary fibrosis (IPF). We aim to investigate this by assessing unplanned hospitalisation-free survival (defined as time from randomisation to first non-elective hospitalisation, lung transplant or death) and to determine whether any effect relates to changes in infection and/or markers of disease control and neutrophil activity., Methods/design: The EME-TIPAC trial is a double-blind, placebo-controlled, randomised, multicentre clinical trial. A total of 330 symptomatic patients, aged 40 years old or older, with IPF diagnosed by a multidisciplinary team (MDT) according to international guidelines and a FVC ≤ 75% predicted will be enrolled. Patients are randomised equally to receive either two tablets of co-trimoxazole 480 mg or two placebo tablets twice daily over a median treatment period of 27 (range 12-42) months. All patients receive folic acid 5 mg daily whilst on the trial IMP to reduce the risk of bone marrow depression. The primary outcome for the trial is a composite endpoint consisting of the time to death, transplant or first non-elective hospital admission and will be determined from adverse event reporting, hospital databases and the Office of National Statistics with active tracing of patients missing appointments. Secondary outcomes include the individual components of the primary outcome, (1) King's Brief Interstitial Lung Disease Questionnaire, (2) MRC Dyspnoea Score, (3) EQ5D, (4) spirometry, (5) total lung-diffusing capacity and (6) routine sputum microbiology. Blood will be taken for cell count, biochemistry and analysis of biomarkers including C-reactive protein and markers of disease. The trial will last for 4 years. Recruitment will take place in a network of approximately 40 sites throughout the UK (see Table 1 for a full list of participating sites). We expect recruitment for 30 months, follow-up for 12 months and trial analysis and reporting to take 4 months., Discussion: The trial is designed to test the hypothesis that treating IPF patients with co-trimoxazole will increase the time to death (all causes), lung transplant or first non-elective hospital admission compared to standard care ( https://www.nice.org.uk/guidance/cg163 ), in patients with moderate to severe disease. The mechanistic aims are to investigate the effect on lung microbiota and other measures of infection, markers of epithelial injury and markers of neutrophil activity., Trial Registration: International Standard Randomised Controlled Trials Number (ISRCTN) Registry, ID: 17464641 . Registered on 29 January 2015.

Background: Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy., Methods: The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses., Results: Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002)., Conclusions: These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.

Background: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development., Results: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available., Conclusions: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Alterations in the gut microbiota are correlated with ailments such as obesity, inflammatory bowel disease, and diarrhea. Up to 60% of individuals traveling from industrialized to developing countries acquire a form of secretory diarrhea known as travelers' diarrhea (TD), and enterotoxigenic Escherichia coli (ETEC) and norovirus (NoV) are the leading causative pathogens. Presumably, TD alters the gut microbiome, however the effect of TD on gut communities has not been studied. We report the first analysis of bacterial gut populations associated with TD. We examined and compared the gut microbiomes of individuals who developed TD associated with ETEC, NoV, or mixed pathogens, and TD with no pathogen identified, to healthy travelers. We observed a signature dysbiotic gut microbiome profile of high Firmicutes:Bacteroidetes ratios in the travelers who developed diarrhea, regardless of etiologic agent or presence of a pathogen. There was no significant difference in α-diversity among travelers. The bacterial composition of the microbiota of the healthy travelers was similar to the diarrheal groups, however the β-diversity of the healthy travelers was significantly different than any pathogen-associated TD group. Further comparison of the healthy traveler microbiota to those from healthy subjects who were part of the Human Microbiome Project also revealed a significantly higher Firmicutes:Bacteriodetes ratio in the healthy travelers and significantly different β-diversity. Thus, the composition of the gut microbiome in healthy, diarrhea-free travelers has characteristics of a dysbiotic gut, suggesting that these alterations could be associated with factors such as travel.

Global climate change is predicted to alter the intensity and duration of droughts, but the effects of changing precipitation patterns on vegetation mortality are difficult to predict. Our objective was to determine whether prolonged drought or above-average precipitation altered the capacity to respond to the individual precipitation pulses that drive productivity and survival. We analyzed 5 yr of data from a rainfall manipulation experiment in piñon-juniper (Pinus edulis-Juniperus monosperma) woodland using mixed effects models of transpiration response to event size, antecedent soil moisture, and post-event vapor pressure deficit. Replicated treatments included irrigation, drought, ambient control and infrastructure control. Mortality was highest under drought, and the reduced post-pulse transpiration in the droughted trees that died was attributable to treatment effects beyond drier antecedent conditions and reduced event size. In particular, trees that died were nearly unresponsive to antecedent shallow soil moisture, suggesting reduced shallow absorbing root area. Irrigated trees showed an enhanced response to precipitation pulses. Prolonged drought initiates a downward spiral whereby trees are increasingly unable to utilize pulsed soil moisture. Thus, the additive effects of future, more frequent droughts may increase drought-related mortality., (© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.)