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Motivation: Nonlinear low-dimensional embeddings allow humans to visualize high-dimensional data, as is often seen in bioinformatics, where datasets may have tens of thousands of dimensions. However, relating the axes of a nonlinear embedding to the original dimensions is a nontrivial problem. In particular, humans may identify patterns or interesting subsections in the embedding, but cannot easily identify what those patterns correspond to in the original data., Results: Thus, we present SlowMoMan (SLOW Motions on MANifolds), a web application which allows the user to draw a one-dimensional path onto a 2D embedding. Then, by back-projecting the manifold to the original, high-dimensional space, we sort the original features such that those most discriminative along the manifold are ranked highly. We show a number of pertinent use cases for our tool, including trajectory inference, spatial transcriptomics, and automatic cell classification., Availability and Implementation: Software: https://yunwilliamyu.github.io/SlowMoMan/; Code: https://github.com/yunwilliamyu/SlowMoMan., Competing Interests: No competing interest is declared., (© The Author(s) 2024. Published by Oxford University Press.)

Objective: Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort., Materials and Methods: We implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019., Results: Loyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences., Discussion: This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis., Conclusion: i2b2 sites can use this approach to select cohorts with mostly complete EHR data., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data., Materials and Methods: Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling., Results: In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers., Discussion: Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power., Conclusion: ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity., Materials and Methods: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site., Results: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review., Discussion: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions., Conclusions: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Motivation: The rapid growth in of electronic medical records provide immense potential to researchers, but are often silo-ed at separate hospitals. As a result, federated networks have arisen, which allow simultaneously querying medical databases at a group of connected institutions. The most basic such query is the aggregate count-e.g. How many patients have diabetes? However, depending on the protocol used to estimate that total, there is always a tradeoff in the accuracy of the estimate against the risk of leaking confidential data. Prior work has shown that it is possible to empirically control that tradeoff by using the HyperLogLog (HLL) probabilistic sketch., Results: In this article, we prove complementary theoretical bounds on the k-anonymity privacy risk of using HLL sketches, as well as exhibit code to efficiently compute those bounds., Availability and Implementation: https://github.com/tzyRachel/K-anonymity-Expectation., (© The Author(s) 2021. Published by Oxford University Press.)

Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers., Materials and Methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics., Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access., Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases., Materials and Methods: We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members., Results: EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete., Discussion and Conclusion: As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects., Materials and Methods: Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language., Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed., Discussions: This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative., Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background and Objective: In 2008 we developed a shared health research information network (SHRINE), which for the first time enabled research queries across the full patient populations of four Boston hospitals. It uses a federated architecture, where each hospital returns only the aggregate count of the number of patients who match a query. This allows hospitals to retain control over their local databases and comply with federal and state privacy laws. However, because patients may receive care from multiple hospitals, the result of a federated query might differ from what the result would be if the query were run against a single central repository. This paper describes the situations when this happens and presents a technique for correcting these errors., Methods: We use a one-time process of identifying which patients have data in multiple repositories by comparing one-way hash values of patient demographics. This enables us to partition the local databases such that all patients within a given partition have data at the same subset of hospitals. Federated queries are then run separately on each partition independently, and the combined results are presented to the user., Results: Using theoretical bounds and simulated hospital networks, we demonstrate that once the partitions are made, SHRINE can produce more precise estimates of the number of patients matching a query., Conclusions: Uncertainty in the overlap of patient populations across hospitals limits the effectiveness of SHRINE and other federated query tools. Our technique reduces this uncertainty while retaining an aggregate federated architecture.

Objective: Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software., Materials and Methods: Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions., Results: The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA., Discussion: We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing., Conclusions: The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases.

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.

The authors developed a prototype Shared Health Research Information Network (SHRINE) to identify the technical, regulatory, and political challenges of creating a federated query tool for clinical data repositories. Separate Institutional Review Boards (IRBs) at Harvard's three largest affiliated health centers approved use of their data, and the Harvard Medical School IRB approved building a Query Aggregator Interface that can simultaneously send queries to each hospital and display aggregate counts of the number of matching patients. Our experience creating three local repositories using the open source Informatics for Integrating Biology and the Bedside (i2b2) platform can be used as a road map for other institutions. The authors are actively working with the IRBs and regulatory groups to develop procedures that will ultimately allow investigators to obtain identified patient data and biomaterials through SHRINE. This will guide us in creating a future technical architecture that is scalable to a national level, compliant with ethical guidelines, and protective of the interests of the participating hospitals.

A combination of recombinant human (rh) insulin-like growth factor-I (IGF-I) (25 nM) and the maturation-inducing hormone (MIH), 17,20beta,21-trihydroxy-4-pregnen-3-one (20beta-S; 72.5 nM), induced germinal vesicle breakdown (GVBD) in ovarian follicles of white bass incubated in vitro, whereas a four times greater concentration of each hormone was ineffective alone. These results indicate that IGF-I induces oocyte maturational competence (OMC) but not meiotic resumption in white bass. Culture medium concentrations of 20beta-S remained below detection limits for ovarian fragments incubated with rhIGF-I. Actinomycin D blocked GVBD in response to hCG but not to rhIGF-I plus 20beta-S, suggesting that IGF-I requires de novo translation but not transcription to induce OMC. Gap junction uncouplers, 1-octanol and 1-heptanol, and the phosphatidylinositiol 3-kinase (PI 3-K) inhibitors, wortmannin and LY 294002, attenuated hCG-, 20beta-S-, and rhIGF-I plus 20beta-S-induced GVBD. Although these inhibitors reduced hCG-induced progestin release, PI 3-K inhibitors did not alter MIH synthesis in some incubations and addition of 20beta-S to the incubations did not fully overcome the effects of either class of inhibitors, suggesting that decreasing MIH production is not their only inhibitory effect on gonadotropin (GtH) action. Our data suggest that gap junctions and PI 3-K activity are necessary for GtH and IGF-I to induce and maintain OMC in white bass. The induction of OMC but not meiotic resumption by IGF-I in white bass, compared with the induction of meiotic resumption but not OMC by IGF-I discovered in the congeneric striped bass suggests rapid evolution of the reproductive actions of IGF-I among temperate basses (genus Morone).

The objectives of this study were to 1) purify and characterize vitellogenin-derived yolk proteins of white perch (Morone americana), 2) develop a nonisotopic receptor binding assay for vitellogenin, and 3) identify the yolk protein domains of vitellogenin recognized by the ovarian vitellogenin receptor. Four yolk proteins derived from vitellogenin (YP1, YP2 monomer [YP2m] and dimer [YP2d], and YP3) were isolated from ovaries of vitellogenic perch by selective precipitation, ion exchange chromatography, and gel filtration. The apparent molecular masses of purified YP1, YP2m, and YP2d after gel filtration were 310 kDa, 17 kDa, and 27 kDa, respectively. YP3 appeared in SDS-PAGE as a approximately 20-kDa band plus some diffuse smaller bands that could be visualized by staining for phosphoprotein with Coomassie Brilliant Blue complexed with aluminum nitrate. Immunological and biochemical characteristics of YP1, YP2s, and YP3 identified them as white perch lipovitellin, beta'-components, and phosvitin, respectively. A novel receptor-binding assay for vitellogenin was developed based on digoxigenin (DIG)-labeled vitellogenin tracer binding to ovarian membrane proteins immobilized in 96-well plates. Lipovitellin from white perch and vitellogenin from perch and other teleosts effectively displaced specifically bound DIG-vitellogenin in the assay, but phosvitin and the beta'-component could not, demonstrating for the first time that the lipovitellin domain of teleost vitellogenin mediates its binding to the oocyte receptor. Lipovitellin was less effective than vitellogenin in this regard, suggesting that the remaining yolk protein domains of vitellogenin may interact with its lipovitellin domain to facilitate binding of vitellogenin to its receptor.

In mammals, skeletal muscle mass is negatively regulated by a muscle-derived growth/differentiating factor named myostatin (MSTN) that belongs to the transforming growth factor-beta superfamily. Although putative MSTN homologs have been identified from several vertebrates, nonmammalian orthologs remained poorly defined. Thus, we isolated and characterized MSTN complementary DNA clones from the skeletal muscle of the tilapia Oreochromis mossambicus and the white bass Morone chrysops. The nucleic and amino acid sequences from both fish species are highly homologous to the previously identified mammalian and avian orthologs, and both possess conserved cysteine residues and putative RXXR proteolytic processing sites that are common to all transforming growth factor-beta family members. Western blotting of conditioned medium from human embryonal kidney (HEK293) cells overexpressing a His-tagged tilapia MSTN indicates that the secreted fish protein is processed in a manner similar to mouse MSTN. However, in contrast to mice, MSTN expression in tilapia is not limited to skeletal muscle as it occurs in many tissues. Furthermore, the timing of MSTN expression in developing tilapia larvae coincides with myogenesis. These results suggest that the biological actions of MSTN in the tilapia and possibly in other fishes may not be limited to myocyte growth repression, but may additionally influence different cell types and organ systems.

Recombinant human (rh) insulin-like growth factor-I (IGF-I) was more potent than rhIGF-II at inducing in vitro germinal vesicle breakdown (GVBD), a marker for resumption of meiosis, in oocytes of striped bass. Treatment of ovarian fragments containing oocytes in intact follicles with rhIGF-I increased concentrations of estradiol-17beta and maturation-inducing steroid (MIS) 17,20beta, 21-trihydoxy-4-pregnen-3-one (20beta-S) in the culture medium and decreased testosterone levels. The follicles were too immature for oocytes to complete GVBD in response to 20beta-S (MIS incompetent) or hCG. Addition of 20beta-S to cultures did not increase the percentage of oocytes completing GVBD in response to rhIGF-I or rhIGF-II. Bovine insulin was without effect on GVBD or steroid production. Incubation of MIS-competent follicles with actinomycin D, cyanoketone, trilostane, 1-heptanol, or 1-octanol had no effect on rhIGF-I-induced GVBD, but attenuated hCG-induced GVBD and 20beta-S production. Cycloheximide inhibited rhIGF-I-induced GVBD. Collectively, these observations indicate that IGF-I can induce GVBD via MIS- and transcription-independent pathways without coupled gap junctions between oocytes and granulosa cells or among granulosa cells, but requires protein synthesis to do so. An rhIGF-I analogue that does not bind IGF-binding proteins, des(1,3)IGF-I, was more potent than rhIGF-I in inducing GVBD, suggesting ovarian IGF-binding proteins may inhibit IGF-I action.