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The overall goals of this query are to 1) characterize the growth in patients receiving Paxlovid over time and 2) examine Paxlovid use by patient characteristics, including by acute SARS CoV-2 infection profile and healthcare utilization. Patients with a COVID index date or a visit date between 12/1/2021 (7 days before EUA) and 5/30/2022 are included in the results. Based on the limited data currently available, we provide counts of patients who received paxlovid by site and characteristics of patients who received paxlovid at two time points (12/1/2021-3/31/22 and 4/1/22-5/30/22).

This query examines the effect of vaccination (prior to COVID-19 infection) on the risk of PASC. When considering patients across all sites we see a modest protective effect of vaccination on PASC with an odds ratio of 0.81 (0.79, 0.83). Limiting to sites with more reliable vaccination information yields an odds ratio of 0.95 (0.91, 0.99). The sensitivity of the effect to which sites are included suggests the presence of potential confounding factors at the site level.

The overall goal is to identify the relative risk of developing PASC for patients with three specific pre-existing chronic conditions. Here, Obstructive Sleep Apnea (OSA) and Multiple Sclerosis (MS) are each defined by diagnosis codes, while Diabetes Melitus (DM) is defined by a combination of diagnosis codes and Hemoglobin A1c levels. In order to investigate the confounding effect of obesity (OB), we further defined obesity with a combination of diagnosis codes and observed BMI data. These pre-existing conditions showed increased odds ratios for developing PASC. Of the four, OSA had the highest adjusted odds ratio, followed by MS. The odds ratio for DM was small, with obesity having a stronger correlation to PASC than diabetes.

The overall goal of this query is to quantify deaths (due to any cause) in COVID-19 positive individuals. Here, we look at deaths across subpopulations defined by age, sex, race, hospitalization, and discharge disposition. We repeat all analyses for two definitions of COVID-19 infection (lab-based or diagnosis-based), as well as for either the overall N3C cohort or for a subset composed of only the RECOVER Autopsy sites. Finally, we provide evidence of the trajectory and time course of death after COVID-19 infection. Principally, we find that the majority of deaths occur soon after COVID-19 infection (within 30 days). As a result, this work suggests that mortality more than 90 days after COVID-19 infection is not worse in patients with PASC.

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.

Do we see differences in PASC across time and region?

Query Report #1 for the RECOVER program focused on evaluating differences in acute visit characteristics in patients that will develop PASC in the National Covid Cohort Collaborative.

Do we see increased indicators of inflammation or other objective laboratory measurements in PASC patients compared to COVID-resolved patients?

As part of the Biden Cancer Moonshot Data Sharing Panel, a rich, interoperable Data Ecosystem was envisioned. The Cancer Research Data Commons (CRDC) is an audacious and ambitious NCI plan to integrate federated sources of cancer data, including clinical, genomic, proteomic, imaging, and canine, among others. Ultimately, the CRDC aims tobe a suite of repositoriesfor all information relating to patients and research subjects with cancer. However, because each CRDC resource has developed its own approaches to various aspects of the data life cycle, an assessment was performed to understand the current state ofinteroperability across CRDC resources, their needs, and provide recommendations for alignment and improvement towards the goal of creation of a rich analytical ecosystem. Note that the recommendations are not included here.

This entry contains thepodium abstract that was presented at the 2021American Medical Informatics Association Virtual Informatics Summit (https://amia.org/education-events/amia-2021-virtual-informatics-summit).

Medical and ontology experts met at a workshop in April 2021 to investigate a new classification for the Mondo ontology that may complement/correspond with the clinical community's approach to disease classification. The ���Harrison's Principles of Internal Medicine��� textbook (below called ���Harrison���) has been used as a landmark in medicine, for both trainees and practicing clinicians. The goal of this workshop was to review the high-level classification of Mondo to reflect the organization of Harrison's book of medicine, and thereby offer a clinically oriented view of Mondo (called ���Harrison view���). This report summarizes the changes made to the Mondo ontology and the creation of a new ���Harrison view��� of Mondo., This work was supported by NHI/NHGRI 1RM1HG010860-01

NIH requestedpublic comment on the use of CDEs, particularly in the context of COVID-19 research, including opportunities for advancing research with CDEs, challenges to adopting CDEs, and guidance or tools that could facilitate use of CDEs. Here, members of theNCI funded Center for Cancer Data Harmonization, the Pediatric Data Commons, the NCATS funded Center for Data to Health, describe our extensive use ofCDEs for prospective studies and data harmonization efforts. We participate in many SDOs such as HL7, ISO, CDISC, GA4GH, WHO, etc. and try to promote computational encoding and use of CDEs in these contexts.

Decades of reductionist scientific approaches have led to spectacular progress and the proliferation of biological sub-disciplines, each spawning its own technical and social practices regarding data. This fragmented landscape poses substantial hurdles to the multi-disciplinary approaches needed to address pressing societal challenges. Data integration is key to the reintegration of biology and the pursuit of global questions such as climate change, biodiversity loss, and sustainable ecosystem management. Here, we define the primary challenges in data integration and present a vision for a Data as a Service (DaaS) oriented architecture that enables frictionless data reuse, hypothesis testing, and discovery. The proposed data integration infrastructure includes standards development, a suite of tools and services, and strategies for education and sustainability.

Toxicology as a discipline is moving toward increased standardization of methods for testing and reporting end points in model organisms for improved, cross-laboratory data integration. Labs engaged in high-throughput analyses have established handbooks to internally standardize results, but these standards are rarely applied across laboratories. This poses a problem for integrating and comparing data across labs, which could support more robust corroboration in the confidence of results from toxicological assessments. To set a baseline, an informal survey was created asking participants to score 24 lateral images of zebrafish larvae for chemical-induced malformations (i.e., abnormalities) according to their laboratory practices and standards. Participants were allowed to answer using free text. Eighteen researchers from fourteen different labs participated. The free text results included 1748 terms which were mapped to 48 traits from the Zebrafish Phenotype Ontology (ZP) for comparison. Both the reported endpoints and the terms used to describe them were very heterogeneous and had little agreement - even the most straightforward of endpoints, normal and dead embryos, were not consistently identified. Abnormalities of the heart and yolk were the most consistently identified (67% and 63% agreement, respectively) while abnormalities of the pectoral fin and the gut were least consistently identified (6% and 11% agreement, respectively). Abnormalities of the gut, pectoral fin, and otic vesicles were the most heterogeneously described while abnormalities of the trunk, snout, and heart were the most homogeneously described. Participants were able to score every image, all images were lateral views at stage 96 hours post fertilization (hpf) which were taken with the VAST (Vertebrate Automates Screening Technology)-System. It is clear from these results that the community requires improved standards for defining and scoring toxicological endpoints. Future work will include conducting a study that includes a controlled vocabulary with textual definitions for scoring embryo images, to determine if this improves consistency.

Semantic technology has the potential to advance research by acting as a bridge across bespoke standards. This has already been demonstrated through projects like the Monarch Initiative (Shefchek et al., 2020) and Phenoscape (Edmunds et al., 2015). Both of these projects are biologically-focused repositories for relating genes and phenotypes, but the environment plays an important role in the observable traits of organisms. To represent environmental exposures in the Monarch knowledge graph we created the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) and proposed a model for linking environments to diseases and phenotypes. Development of the ontology and the model was guided by biomedicine and toxicology, two user communities that have a keen interest in understanding the effect of elements of the environment on gene expression, observable phenotypes, and disease progression. This talk will present the semantic model and review the process for its development within the toxicology and biomedical communities.

Background: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently available data links a majority of known coding human genes to phenotype, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist. Their development must be a bottom-up, community effort. Objectives: Our objective is to foster development of data reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. Methods: To this end, the Computable Exposures Workshop brought together a diverse community of toxicologists, computer scientists, and ontologists in September 2019 to discuss the nature of exposure data and how it might best be integrated with genomic and phenomic data. Results: This workshop presented a preliminary semantic data model, developed several use cases and competency questions, and laid the groundwork for sustained collaboration, including planning of a larger conference in 2020. Discussion: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Future collaboration over the longer-term will include an assessment of exposure data resources from the perspective of data modeling and integration and continued development of the Adverse Outcome Pathway (AOP) Wiki and AOP ontology.

Modern toxicology is evolving to leverage data science methodologies to better address complex public health and environmental concerns. Understanding the potential adverse impacts of environmental exposures requires working across a variety of domains and data types that are often siloed and require manual curation and extraction. Ontologies and semantic engineering can facilitate meaningful data integration, but existing semantic standards have not been widely used in toxicology. Development of semantic standards for toxicology requires sustained interdisciplinary collaborations. One place where the community came together to discuss this need is the “Computable Exposures” workshop, held at Oregon State University in September 2019. Ontologists, toxicologists, epidemiologists, exposure scientists, ecotoxicologists, clinicians, computer scientists, computational biologists, and data scientists from academia, government, and industry were in attendance. Here we describe community-building efforts, standards development, and plans for future work. Objectives include building a semantic exposure data model using the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), developing toxicology-driven use cases and competency questions, creating a mailing list, and planning a larger computable exposures conference. Twelve use cases were developed during the workshop, for example, using semantic technology to complete Adverse Outcomes Pathway (AOP) and Aggregate Exposure Pathway (AEP) given the initiating and terminal key events. Broader adoption of ontologies, together with increased data sharing, has the potential to improve a toxicologist’s ability to integrate, navigate, and analyze vast amounts of heterogeneous data—allowing for more rapid safety assessment of chemical and environmental exposures, and increased understanding of underlying biological mechanisms across species.

Data Sharing Plan as proposed for Monarch

CTSA Program Center for Data to Health (CD2H) Response to the NIH Draft Policy for Data Management and Sharing and Supplemental Draft Guidance

The Phenotypes Traversing All the Organisms Workshop (POTATO) series brings together phenotype ontology curators and developers to reconcile phenotype ontologies across species. In the first workshop (Oregon, 2018), we introduced methods and tools for community-driven development of logical definitions for phenotypes. Together we resolved some of the logically divergent definitions across organisms. As a result, 14 phenotype ontologies and databases covering all major model organisms joined a common Phenotype Ontology Reconciliation Effort with bi-weekly meetings and focus groups. A report of the first installment of the workshop, including a detailed description outlining the motivation of the workshop series, is available on Zenodo (1). The central purpose of this workshop was to bring together users and developers of phenotype ontologies and to provide them with a forum to share, learn and debate. The second edition of the workshop further aimed to address two of the fundamental bottlenecks identified in the course of these community efforts: The Phenotype And Trait Ontology (PATO), an essential driver of inference in phenotype ontologies, has limitations that can make it hard to understand and use in defining phenotypes. For example, textual definitions need to be added and improved, and hierarchies refined to be more intuitive. The phenotype ontology community needs efficient ways to directly contribute to anatomical and cell concepts in the species-independent anatomy ontology Uberon and the Cell Ontology (CL)., The workshop was funded by NIH Office of the Director Grant #5R24OD011883, as well as by the phenomics conference grant 5U13CA221044-03. Coffee and other sustenance was kindly provided by the International Society for Biocuration. A big thank you goes to Midori Harris who oversaw the workshop logistics and ensured a surprise-free workshop experience! ERRATA: The funding statement in the PDF is replaced by the one above.

Logical definitions, in particular those following the Entity-Quality approach, are increasingly used to drive automated classification of phenotypes and integrate phenotypes across species semantically. Over the years, the lack of consistent and widespread use of common standards resulted in conceptually equivalent or similar phenotypes with logically divergent definitions. Recently, pattern-based approaches such as Dead Simple Ontology Design Patterns (DOSDP) have emerged as a way to reconcile such divergent definitions on a large scale. To facilitate the widespread adoption of DOSDP-based phenotype ontology development and to organise the reconciliation process involving key model organism communities, we have established the Phenotype Ontologies Traversing All The Organisms (POTATO) Workshop Series. These workshops are part of the NIH-funded “Forums for Integrative Phenomics” conference award, which provide a venue for phenotype ontology developers and phenomics informaticians to align their goals and technical approaches. The first POTATO workshop, in August 2018, provided training in cutting-edge tools and techniques for developing and releasing ontologies using design patterns and automated imports. This workshop also featured collaborative exercises in reconciling divergent logical definitions drawn from major phenotype ontologies that resulted in a set of general recommendations for how to improve phenotype ontologies and their development as well as a set of reconciled definitions. As a result of the workshop, many of the attendees have joined a phenotype ontology reconciliation effort, aimed at developing and implementing common design patterns across a range of phenotype ontologies. We thus plan to hold additional POTATO workshops., Funding: This workshop was supported by an NIH conference grant "Forums for Integrative phenomics" to Melissa Haendel and Peter Robinson: 1U13CA221044.

People from a diverse array of backgrounds play important roles in research, often in ways that cannot be quantified through traditional metrics of scholarly impact. This demands better approaches to evaluate and ultimately communicate scholarly outcomes beyond the narrow criteria of publications and grants. It is imperative to develop a structure to track a much wider diversity of contributor roles and research objects - and do so in a manner that is easily populated with real data. This presentation will share details about the project, our team and approach, upcoming opportunities to collaborate, metrics for success, and integration and application of this work to date., Preprint submitted to RO2018 workshop at IEEE eScience Conference 2018

This paper describes an effort to increase accessibility of the Human Phenotype Ontology to patients and non-medical expertsby adding lay-person synonyms and terminology.

Life science data is evolving to be ever larger, more distributed, and more natively web-based. However, our collective handling of identifiers has lagged behind these advances. Diverse identifier issues (for instance “link rot” and “content drift”) have hampered our ability to integrate data and derive new knowledge from it. Optimizing web-based identifiers is harder than it appears and no single scheme is perfect: Identifiers are reused in different ways for different reasons, by different consumers. Moreover, digital entities (e.g., files), physical entities (e.g., biosamples), and descriptive entities (e.g., ‘mitosis’) have different requirements for identifiers. Nevertheless, there is substantial room for improvement throughout the life sciences and several other groups have been converging on identifier standards that are broadly applicable. Building on these efforts and drawing on our experience, we focus on the use case of large-scale data integration: we outline the identifier qualities and best practices that we feel are most important in this context. Specifically, we propose actions that providers of online databases (repositories, registries, and knowledgebases) should take when designing new identifiers or maintaining existing ones (Rules 1-9). In Rule 10, we conclude with guidance to data integrators and redistributors on how best to reference identifiers from these diverse sources. This article may also be useful to data generators and end users as it offers insight into the issues associated with data provision in a web environment. We call upon data providers to take a long-term view of their entities’ scope and lifecycle, and to consider existing identifier platforms and services. Rule 1. Use established identifiers Rule 2. Design identifiers for use by others Rule 3. Help local identifiers travel well: document Prefix and Namespace Rule 4. Opt for simple durable web resolution Rule 5. Avoid embedding meaning Rule 6. Make URIs clear and findable Rule 7. Implement a version management policy Rule 8. Do not re-assign or delete identifiers Rule 9. Document the identifiers you issue and use Rule 10. Reference responsibly

In the life sciences, problems with identifiers impede the flow and integrity of information. This is especially challenging within “synthesis research” disciplines such as systems biology, translational medicine, and ecology. Implementation-driven initiatives such as ELIXIR, BD2K, and others have therefore been actively working to understand and address underlying problems with identifiers. Good, global-scale, persistent identifier design is harder than it appears, and is essential for data to be Findable, Accessible, Interoperable, and Reusable (Data FAIRport principles). Here, we build on emerging conventions and existing general recommendations and summarise the identifier characteristics most important to optimising the utility of life-science data. We propose actions to take in the identifier ‘green field’ and offer guidance for using real-world identifiers from diverse sources.

Semantic phenotyping has been shown to be an effective means to aid variant prioritization and characterization by comparison to both known Mendelian diseases and across species with animal models (Robinson et al 2013). This process, whereby symptoms and characteristic phenotypic findings are curated with species-specific ontology terms, has generated a baseline set of disease phenotype descriptions for more than 7,000 Mendelian diseases (Kohler et al 2014a) as well as many thousands of descriptions of additional animal models. By leveraging the knowledge encoded in the ontology graph and methods drawn from information theory, similarities can be computed between any two sets of phenotype descriptions (Washington et al 2009). This very powerful technique has the potential to be used for disease diagnosis, particularly for novel and rare diseases when the underlying genetic cause is unknown. The robustness of semantic similarity methods is heavily dependent on the quality of both the knowledgebase as well as the phenotype profile being studied. Therefore, capturing the highest quality phenotypic profiles is necessary. Until now, these phenotypic profiles have been typically captured by specialized curators, but as we want to move this technique into the diagnostic setting it will need to move into a physician’s hands. This process of acquiring structured phenotype annotations for individual patients may seem daunting and unnecessarily complex for physicians with high demands on their time. Annotation tools such as Phenotips (Girdea et al 2013) greatly facilitate recording rigorous phenotype annotations in the clinic, but do not themselves provide guidance about what constitutes annotations sufficient for comparative phenotype analysis. Since clinicians are not used to providing structured phenotype data, it is necessary to provide a measurement of how a given patient phenotype profile compares against the corpus of available genotype-phenotype annotations, including that of known diseases, animal models, and other patients in the system. A metric to gauge overall complexity and diagnostic capability of a phenotype profile generated in this way would greatly enhance the ability to use structured phenotyping in the clinical setting for comparative analysis. Conversely, such a metric can also be utilized in the context of any systematic model organism phenotyping efforts. Here, we present a method to assess the sufficiency of a phenotype profile, by investigating the necessary and sufficient information characteristics required to identify disease similarity based on phenotypes alone. This scoring method is being provided as a REST service through the Monarch Initiative API., This article is no longer available at its original location on the phenoday website.

Nutri-informatics aims to computationally integrate and analyze nutrition study datasets in order to disentangle the interactions between an organism and its nutritional environment. Fueled by an interest in how food, nutrients, and nutrition sociology impact health, and a recent push towards “big data”, nutri-informatics is essential to incorporating nutrition into computational biomedical sciences. Nutrition is one of the most integral components to human life, and it impacts individuals far beyond just nutrient provisions. For example, nutrition plays a role in cultural practices, interpersonal relationships, and body image. Despite this, integrated computational investigations have been limited due to challenges within nutri-informatics and nutrition data. Nutri-informatics suffers from a lack of standardization with a wide array of groups working on similar projects with no community-wide development principles to ensure interoperability and cohesion between nutri-informatics and other biomedical resources. While a large number of resources for nutri-informatics are available, much of nutrition is underrepresented. This may be due to how expansive and heterogeneous nutrition is as a field, increasing the difficulty of data modeling. Approaches to formalize nutrition research language and connect standardized terminologies across biomedical fields have been initiated through the use of biomedical ontologies and computational nutrition data resources. While a variety of nutrition-related ontologies such as Food Ontology and the Ontology for Nutritional Studies have been initiated, they are still in development and require further attention from nutrition researchers and biomedical ontologists. Should nutrition data continue to be produced with no standardization of language, documentation specifications, or requirements for data reuse, nutri-informatics investigations will continue to struggle with incompatible data. In efforts to support nutri-informatics, the community must encourage standards for nutrition data production, reuse, and publication. Academic journals as well as members from nutrition research and biomedical ontology communities should promote standardization of language, data interoperability, and FAIR principles.

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch, and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch, and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.&nbsp

In the life sciences, problems with identifiers impede the flow and integrity of information. This is especially challenging within “synthesis research” disciplines such as systems biology, translational medicine, and ecology. Implementation-driven initiatives such as ELIXIR, BD2K, and others have therefore been actively working to understand and address underlying problems with identifiers. Good, global-scale, persistent identifier design is harder than it appears, and is essential for data to be Findable, Accessible, Interoperable, and Reusable (Data FAIRport principles). Here, we build on emerging conventions and existing general recommendations and summarise the identifier characteristics most important to optimising the utility of life-science data. We propose actions to take in the identifier ‘green field’ and offer guidance for using real-world identifiers from diverse sources., ORCIDs corresponding to the authors are: http://orcid.org/0000-0002-9353-5498 http://orcid.org/0000-0003-4155-5910 http://orcid.org/0000-0002-2513-5396 http://orcid.org/0000-0002-1010-3121 http://orcid.org/0000-0003-4727-9435 http://orcid.org/0000-0002-9091-5938 http://orcid.org/0000-0003-3499-8262 http://orcid.org/0000-0001-9823-1621 http://orcid.org/0000-0002-3469-4923 http://orcid.org/0000-0001-9114-8737 http://orcid.org/0000-0001-8479-0262 http://orcid.org/0000-0001-6867-9425 http://orcid.org/0000-0001-6666-1520 http://orcid.org/0000-0001-5404-7670 http://orcid.org/0000-0002-0643-3144 http://orcid.org/0000-0002-2036-8350 http://orcid.org/0000-0002-4625-743X http://orcid.org/0000-0002-6309-7327 http://orcid.org/0000-0002-1615-2899 http://orcid.org/0000-0001-5454-2815 http://orcid.org/0000-0002-1611-6935 http://orcid.org/0000-0002-9533-5684 http://orcid.org/0000-0002-1034-5171 http://orcid.org/0000-0002-4980-3512 http://orcid.org/0000-0002-6601-2165 http://orcid.org/0000-0001-9853-5668 http://orcid.org/0000-0001-5306-5690 http://orcid.org/0000-0002-5595-689X http://orcid.org/0000-0002-0405-8854 http://orcid.org/0000-0003-4958-0184 http://orcid.org/0000-0001-7020-1236 http://orcid.org/0000-0001-8936-9143 http://orcid.org/0000-0003-3156-2105 http://orcid.org/0000-0002-1279-5133 http://orcid.org/0000-0003-1219-2137 http://orcid.org/0000-0003-3035-4195