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Whole genome duplications (WGDs) are major events that drastically reshape genome architecture and are causally associated with organismal innovations and radiations1. The 2R Hypothesis suggests that two WGD events (1R and 2R) occurred during early vertebrate evolution2,3. However, the veracity and timing of the 2R event relative to the divergence of gnathostomes (jawed vertebrates) and cyclostomes (jawless hagfishes and lampreys) is unresolved4-6 and whether these WGD events underlie vertebrate phenotypic diversification remains elusive7. Here we present the genome of the inshore hagfish, Eptatretus burgeri. Through comparative analysis with lamprey and gnathostome genomes, we reconstruct the early events in cyclostome genome evolution, leveraging insights into the ancestral vertebrate genome. Genome-wide synteny and phylogenetic analyses support a scenario in which 1R occurred in the vertebrate stem-lineage during the early Cambrian, and the 2R event occurred in the gnathostome stem-lineage in the late Cambrian after its divergence from cyclostomes. We find that the genome of stem-cyclostomes experienced two additional, independent genome duplications (herein CR1 and CR2). Functional genomic and morphospace analyses demonstrate that WGD events generally contribute to developmental evolution with similar changes in the regulatory genome of both vertebrate groups. However, appreciable morphological diversification occurred only after the 2R event, questioning the general expectation that WGDs lead to leaps of morphological complexity7.

BACKGROUND Despite the potential of artificial intelligence/machine learning (AI/ML)-based medical modeling software (MMS) to revolutionize healthcare, the anticipated benefits have yet to be realized due to acknowledged gaps in translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a significant barrier to the successful translation of software from research settings to clinical practice. OBJECTIVE This study aimed to scope the current landscape of AI/ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows. METHODS A scoping review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed was searched using MeSH key concepts of AI/ML, ethical considerations, and explainability to identify publications detailing AI/ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use “documentation” as a key concept but as an inclusion criterion. A two-stage screening process (title and abstract screening and full-text review) was conducted by an independent reviewer. A data extraction template was utilized to record publication-related information, barriers to developing ethical and explainable MMS, available standards, regulations, frameworks, or governance strategies related to documentation, and recommendations for documentation for papers that met inclusion criteria. RESULTS Of the total 115 papers retrieved, 21 (18%) articles met the requirements for inclusion. Ethics and explainability were investigated in the context of AI/ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future work were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and interpretability. Recommendations identified within the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI/ML-based MMS. CONCLUSIONS The ability of MMS to deliver on expectations is dependent on resolving barriers to translation, including those identified within this scoping review related to bias, accountability, governance, and interpretability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS. CLINICALTRIAL

BackgroundTranslation of artificial intelligence/machine learning (AI/ML)-based medical modeling software (MMS) into clinical settings requires rigorous evaluation by interdisciplinary teams and across the AI lifecycle. The fragmented nature of available resources to support MMS documentation limits the transparent reporting of scientific evidence to support MMS, creating barriers and impeding the translation of software from code to bedside.ObjectiveThe aim of this paper is to scope AI/ML-based MMS documentation practices and define the role of documentation in facilitating safe and ethical MMS translation into clinical workflows.MethodsA scoping review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE (PubMed) was searched using MeSH key concepts of AI/ML, ethical considerations, and explainability to identify publications detailing AI/ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use “documentation “ as a key concept but rather as an inclusion criterion. A two-stage screening process (title and abstract screening and full-text review) was conducted by an independent reviewer. A data extraction template was utilized to record publication-related information, barriers to developing ethical and explainable MMS, available standards, regulations, frameworks, or governance strategies related to documentation, and recommendations for documentation for papers that met inclusion criteria.ResultsOf the total 115 papers, 21 (18%) articles met the requirements for inclusion. Data regarding the current state and challenges of AI/ML-based documentation was synthesized and themes including bias, accountability, governance, and interpretability were identified.ConclusionsOur findings suggest that AI/ML-based MMS documentation practice is siloed across the AI life cycle and there exists a gray area for tracking and reporting of non-regulated MMS. Recommendations from the literature call for proactive evaluation, standards, frameworks, and transparency and traceability requirements to address ethical and explainability barriers, enhance documentation efforts, provide support throughout the AI lifecycle, and promote translation of MMS. If prioritized across multidisciplinary teams and across the AI lifecycle, AI/ML-based MMS documentation may serve as a method of coordinated communication and reporting toward resolution of AI translation barriers related to bias, accountability, governance, and interpretability.

BACKGROUND Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. Transparency, accountability, suitability, and adaptability may be bolstered by clinician engagement through a direct empathetic approach aimed at determining complex user requirements of implementation, usability, and workflow integration. OBJECTIVE We aimed to utilize a structured user-centered design approach (double-diamond design framework) to 1) qualitatively explore clinicians' experience with the current asthma management system, 2) identify user requirements to improve algorithm explainability and A-GPS prototype, and 3) identify potential barriers to ML-based CDS system use. METHODS At the 'discovery' phase, we first shadowed to understand the practice context. Then, semi-structured interviews were conducted online with 14 clinicians who provide asthma care at two outpatient facilities. Participants were asked about their current difficulties in gathering information for pediatric asthma patients, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the 'define' phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical 'how might we' research questions to guide model development and implementation. RESULTS We identified user requirements and potential barriers associated with three overarching themes: 1) Usability and Workflow Aspects of the ML System, 2) User Expectations and Algorithm Explainability, and 3) Barriers to Implementation in Context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal machine-learning algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. CONCLUSIONS As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semi-structured interviews. Our focus on meeting the needs of the practice with machine learning technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

BackgroundPersonalized asthma management depends on a clinician’s ability to efficiently review patient’s data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. Transparency, accountability, suitability, and adaptability may be bolstered by clinician engagement through a direct empathetic approach aimed at determining complex user requirements of implementation, usability, and workflow integration.ObjectivesWe aimed to utilize a structured user-centered design approach (double-diamond design framework) to 1) qualitatively explore clinicians’ experience with the current asthma management system, 2) identify user requirements to improve algorithm explainability and A-GPS prototype, and 3) identify potential barriers to ML-based CDS system use.MethodsAt the ‘discovery’ phase, we first shadowed to understand the practice context. Then, semi-structured interviews were conducted online with 14 clinicians who provide asthma care at two outpatient facilities. Participants were asked about their current difficulties in gathering information for pediatric asthma patients, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the ‘define’ phase, a synthesis analysis was conducted to converge key results from interviewees’ insights into themes, eventually forming critical ‘how might we’ research questions to guide model development and implementation.ResultsWe identified user requirements and potential barriers associated with three overarching themes: 1) Usability and Workflow Aspects of the ML System, 2) User Expectations and Algorithm Explainability, and 3) Barriers to Implementation in Context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients’ high risks and take proactive actions to manage asthma efficiently and effectively. For optimal machine-learning algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants.ConclusionsAs part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semi-structured interviews. Our focus on meeting the needs of the practice with machine learning technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Soft-bodied cephalopods such as the octopus are exceptionally intelligent invertebrates with a highly complex nervous system that evolved independently from vertebrates. Because of elevated RNA editing in their nervous tissues, we hypothesized that RNA regulation may play a major role in the cognitive success of this group. We thus profiled mRNAs and small RNAs in 18 tissues of the common octopus. We show that the major RNA innovation of soft-bodied cephalopods is a massive expansion of the miRNA gene repertoire. These novel miRNAs were primarily expressed in neuronal tissues, during development, and had conserved and thus likely functional target sites. The only comparable miRNA expansions happened, strikingly, in vertebrates. Thus, we propose that miRNAs are intimately linked to the evolution of complex animal brains.One-Sentence SummarymiRNAs are deeply linked to the emergence of complex brains.

SummaryThe annotation of microRNAs, an important class of post-transcriptional regulators, depends on the availability of transcriptomics data and expert knowledge. This led to a large gap between novel genomes made available and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in MirMachine, our new tool for the annotation of conserved microRNA complements from genomes only. We successfully applied MirMachine to a wide range of animal species, including those with very large genomes, additional genome duplications and extinct species, where smallRNA sequencing will be hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field facilitating automated genome annotation pipelines and deeper studies on the evolution of genome regulation, even in extinct organisms.HighlightsAn annotation pipeline using trained covariance models of microRNA familiesEnables massive parallel annotation of microRNA complements of genomesMirMachine creates meaningful annotations for very large and extinct genomesmicroRNA score to assess genome assembly completenessGraphical abstract

The evolution of specialized cell-types is a long-standing interest of biologists, but given the deep time-scales very difficult to reconstruct or observe. microRNAs have been linked to the evolution of cellular complexity and may inform on specialization. The endothelium is a vertebrate specific specialization of the circulatory system that enabled a critical new level of vasoregulation. The evolutionary origin of these endothelial cells is unclear. We hypothesized that Mir-126, an endothelial cell-specific microRNA may be informative.We here reconstruct the evolutionary history of Mir-126. Mir-126 likely appeared in the last common ancestor of vertebrates and tunicates, a species without an endothelium, within an intron of the evolutionary much older EGF Like Domain Multiple (Egfl) locus. Mir-126 has a complex evolutionary history due to duplications and losses of both the host gene and the microRNA. Taking advantage of the strong evolutionary conservation of the microRNA among Olfactores, and using RNA in situ hybridization (RISH), we localized Mir-126 in the tunicateCiona robusta. We found exclusive expression of the mature Mir-126 in granular amebocytes, supporting a long-proposed scenario that endothelial cells arose from hemoblasts, a type of proto-endothelial amoebocyte found throughout invertebrates.This observed change of expression of Mir-126 from proto-endothelial amoebocytes in the tunicate to endothelial cells in vertebrates is the first direct observation of the evolution of a cell-type in relation to microRNA expression indicating that microRNAs can be a prerequisite of cell-type evolution.Research Highlightsdirect observation of cell-type evolutionhigh conservation of sequence enables for simple RISH experiment of expressionMir-126 follows the evolution of hematopoetic cells to endothelial cells

The appearance of terrestrial land plants is thought to have accompanied an increase in atmospheric oxygen levels, producing the highest O2 concentrations estimated from the geological record, and marking the transition to a permanently oxygenated deep ocean. This Paleozoic oxygenation event, which likely peaked in the Carboniferous Period, was at least partially mediated by the development of recalcitrant, carbon-rich organic compounds in terrestrial plants. A number of studies have argued that shifts in coal formation and paleogeography led to declining preservation of these compounds on land, depressing oxygen levels in the terminal Paleozoic and early Mesozoic. In contrast, we propose that the evolution and diversification of terrestrial herbivores may have limited transport and long-term burial of terrestrial organic compounds in marine sediments, resulting in less organic carbon burial and attendant declines in atmospheric oxygen. This mechanism suggests that interactions among a triad of biological processes—marine photosynthesis, land plant colonization, and the advent of herbivory—may have dictated the long-term redox state of Earth’s surface environments over the Phanerozoic Eon.

Whole-genome duplications (WGDs) have long been considered the causal mechanism underlying dramatic increases to morphological complexity due to the neo-functionalization of paralogs generated during these events. Nonetheless, an alternative hypothesis suggests that behind the retention of most paralogs is not neo-functionalization, but instead the degree of the inter-connectivity of the intended gene product, as well as the mode of the WGD itself. Here, we explore both the causes and consequences of WGD by examining the distribution, expression, and molecular evolution of microRNAs (miRNAs) in both gnathostome vertebrates as well as chelicerate arthropods. We find that although the number of miRNA paralogs tracks the number of WGDs experienced within the lineage, few of these paralogs experienced changes to the seed sequence, and thus are functionally equivalent relative to their mRNA targets. Nonetheless, in gnathostomes, although the retention of paralogs following the 1R autotetraploidization event is similar across the two subgenomes, the paralogs generated by the gnathostome 2R allotetraploidization event are retained in higher numbers on one subgenome relative to the second, with the miRNAs found on the preferred subgenome showing both higher expression of mature miRNA transcripts and slower molecular evolution of the precursor miRNA sequences. Importantly, WGDs do not result in the creation of miRNA novelty, nor do WGDs correlate to increases in complexity. Instead, it is the number of miRNA seed sequences in the genome itself that not only better correlate to instances in complexification, but also mechanistically explain why complexity increases when new miRNA families are established.

Whole genome duplications (WGDs) have long been considered the causal mechanism underlying the dramatic increase in vertebrate morphological complexity relative to invertebrates. This is due to the retention and neo-functionalization of paralogues generated during these events, evolving new regulatory circuits, and ultimately morphological novelty. Nonetheless, an alternative hypothesis suggests that behind the retention of most paralogues is not neo-functionalization, but instead the degree of the inter-connectivity of the intended gene product, as well as the mode of the WGD itself. Here, we explore both the causes and consequences of WGD by examining the distribution, expression, and molecular evolution of microRNAs (miRNAs) in both gnathostome vertebrates as well as chelicerate arthropods. We find that although the number of miRNA paralogues tracks the number of WGDs experienced within the lineage, few of these paralogues experienced changes to the seed sequence, and thus are functionally equivalent relative to their mRNA targets. Nonetheless, the paralogues generated by the gnathostome 2R allotetraploidization event are retained in higher numbers on one sub-genome relative the second, with the miRNAs found on the preferred set of paralogons showing both higher expression of mature miRNA transcripts and slower molecular evolution of the precursor miRNA sequences. Importantly, WGDs do not result in the creation of miRNA novelty, nor do WGDs correlate to increases in complexity. Instead, it is the number of miRNA seed sequences in the genome itself that not only better correlate to instances in complexification, but also mechanistically explain why complexity increases when new miRNA families are established.

Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) systems. In this study, we examine two previously described semantic change laws based on word frequency and polysemy, and analyze how they apply to the clinical domain. We also explore a new facet of change: whether domain-specific clinical terms exhibit different change patterns compared to general-purpose English. Using a corpus spanning eighteen years of clinical notes, we find that the previously described laws of semantic change hold for our data set. We also find that domain-specific biomedical terms change faster compared to general English words.

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world’s largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

Comment on “A microRNA cluster in the Fragile‐X region expressed during spermatogenesis targets FMR1” by Ramaiah et al.[Image: see text]

Resource Description Framework (RDF) is one of the three standardized data formats in the HL7 Fast Healthcare Interoperability Resources (FHIR) specification and is being used by healthcare and research organizations to join FHIR and non-FHIR data. However, RDF previously had not been integrated into popular FHIR tooling packages, hindering the adoption of FHIR RDF in the semantic web and other communities. The objective of the study is to develop and evaluate a Java based FHIR RDF data transformation toolkit to facilitate the use and validation of FHIR RDF data. We extended the popular HAPI FHIR tooling to add RDF support, thus enabling FHIR data in XML or JSON to be transformed to or from RDF. We also developed an RDF Shape Expression (ShEx)-based validation framework to verify conformance of FHIR RDF data to the ShEx schemas provided in the FHIR specification for FHIR versions R4 and R5. The effectiveness of ShEx validation was demonstrated by testing it against 2693 FHIR R4 examples and 2197 FHIR R5 examples that are included in the FHIR specification. A total of 5 types of errors including missing properties, unknown element, missing resource Type, invalid attribute value, and unknown resource name in the R5 examples were revealed, demonstrating the value of the ShEx in the quality assurance of the evolving R5 development. This FHIR RDF data transformation and validation framework, based on HAPI and ShEx, is robust and ready for community use in adopting FHIR RDF, improving FHIR data quality, and evolving the FHIR specification.

A lack of knowledge of the cellular origin of microRNAs (miRNAs) has greatly confounded functional and biomarkers studies. Recently, three studies characterized miRNA expression patterns across >78 human cell types. These combined data expand our knowledge of miRNA expression localization and confirm that many miRNAs show cell-type specific expression patterns.

Summary-level clinical text is an important part of the overall clinical record as it provides a condensed and efficient view into the issues pertinent to the patient, or their "problem list." These problem lists contain a wealth of information pertaining to the patient's history as well as current state and well-being. In this study, we explore the structure of these problem list entries both grammatically and semantically in an attempt to learn the specialized rules, or "sublanguage" that governs them. Our methods focus on a large-scale corpus analysis of problem list entries. Using Resource Description Framework (RDF), we incorporate inferencing and reasoning via domain-specific ontologies into our analysis to elicit common semantic patterns. We also explore how these methods can be applied dynamically to learn specific sublanguage features of interest for a particular concept or topic within the domain.

Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. Methods Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. Results A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.

Free-text problem descriptions are brief explanations of patient diagnoses and issues, commonly found in problem lists and other prominent areas of the medical record. These compact representations often express complex and nuanced medical conditions, making their semantics challenging to fully capture and standardize. In this study, we describe a framework for transforming free-text problem descriptions into standardized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) models. This approach leverages a combination of domain-specific dependency parsers, Bidirectional Encoder Representations from Transformers (BERT) natural language models, and cui2vec Unified Medical Language System (UMLS) concept vectors to align extracted concepts from free-text problem descriptions into structured FHIR models. A neural network classification model is used to classify thirteen relationship types between concepts, facilitating mapping to the FHIR Condition resource. We use data programming, a weak supervision approach, to eliminate the need for a manually annotated training corpus. Shapley values, a mechanism to quantify contribution, are used to interpret the impact of model features. We found that our methods identified the focus concept, or primary clinical concern of the problem description, with an F(1) score of 0.95. Relationships from the focus to other modifying concepts were extracted with an F(1) score of 0.90. When classifying relationships, our model achieved a 0.89 weighted average F(1) score, enabling accurate mapping of attributes into HL7 FHIR models. We also found that the BERT input representation predominantly contributed to the classifier decision as shown by the Shapley values analysis.

Over the past 25 years, new techniques, new discoveries, and new ideas have profoundly impacted our understanding of deuterostome interrelationships and, ultimately, deuterostome evolution. During the late 1980s and early 1990s morphological cladistic analyses made predictions about both taxonomic history and homology, predictions that would be tested independent of the morphological characters themselves with the advent of molecular systematics, the rise of evolutionary developmental biology, and continued exploration of the fossil record. Thanks to these three areas of inquiry, we have gone from scenarios where animals like mobile enteropneust hemichordates and chordates were derived from sessile filter-feeding animals like modern lophophorates, echinoderms, and pterobranch hemichordates, to a new perspective where hemichordates are recognized as the nearest living relative of the echinoderms, and that vagile gill-bearing animals like Cambrian vetulicolians are seen—at least by some—as close to the deuterostome last common ancestor, with both sessility and filter-feeding convergent features of deuterostomes (e.g., echinoderm) and non-deuterostomes (e.g., lophophorates) alike. Although much of the backbone of the new deuterostome phylogeny is supported by multiple independent data sets, as are statements of homology of several different morphological characters, in particular the homology of gill slits across Deuterostomia, nonetheless, the next quarter century of study on this remarkable group of animals promises to be as equally illuminating and exciting as the past quarter century.