In prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio-based metrics to accompany the count-based metrics described previously. We illustrate the mathematical properties of the ratio-based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low-scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis.
Data obtained for population samples studied in clinical trials may not fit normal distributions. These non-normal distributions of samples create analytic and practical difficulties when estimating effect sizes for differences between sampled comparison groups. In these situations, it would be preferable to adopt a non-parametric model-free method to estimate an effect size for the difference between comparison groups in a manner that relies solely on computational analysis of the empirical data.
We generalize the classical effect size for differences as described by Cohen's d coefficient for the difference of the means divided by the standard deviation. Instead, we take an analogous approach for our estimate that we call the Robust Generalized Effect Size (RGES) by calculating the difference of the medians divided by the maximum of the peak half widths for the distributions of the two comparison groups. Thus, we use medians instead of means for the estimates of the measure of central tendency, and peak half widths instead of standard deviations for estimates of the measure of dispersion. Moreover, we define the peak half widths as a generalization of the traditional half width at half maximum (HWHM) of the main peak. Specifically, we propose use of the half width at half absolute height for measures of dispersion around the center of the data distribution. Our method has been defined with both mathematical formulas and MATLAB code with a function rghwhah for the Robust Generalized Half Width Half Absolute Height, and the function rges for the Robust Generalized Effect Size. The function rghwhah finds the width, height and center point of the main peak of the data distribution defined by the rectangular box with width determined by all data points with absolute values greater than half the height of the box. A peak half width obtained from rghwhah does not necessarily equal a peak half width obtained from a traditional HWHM. Then, the function rges computes the effect size comparing the difference between the two distributions each of which may have a different rghwhah width and height for their main peaks. Our method has been generalized sufficiently to analyse multi-modal and other non-normal distributions of data.
We have tested our approach for the estimation of effect sizes with computer simulations comparing pairs of two different normal distributions with a wide range of varying means and standard deviations for the normal distributions. Results for our estimates of effect sizes will also be tabulated for a diverse variety of non-normal distributions compared with each other. These tabulated results of rges coefficient values will provide a reference for comparison of effect sizes for those statisticians and other clinical trial investigators who do not wish to rely solely on estimates for effect sizes based on standard deviations that might not be appropriate for non-normal distributions.
Articles published in Scientific Data by Wilkinson et al. argued for the adoption of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles of data management without citing any of the prior work published by Taswell. However, these principles were first proposed and described by Taswell in 2006 as the foundation for work on the PORTAL-DOORS Project (PDP) and the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure, and have been published in numerous conference presentations, journal articles, and patents. This work on PDP and NPDS has been continuously available since 2007 from a publicly accessible web site at www.portaldoors.org, and discussed in person at conferences with several key authors of the Wilkinson et al. papers. Paraphrasing without citing the PDP and NPDS principles while renaming them as the FAIR principles raises questions about both the ‘FAIRness’ and the fairness of the authors of the Wilkinson et al. papers. Promoting these principles with the use of the term ‘metrics’, which are not metrics by definition of the term metric as used in most fields of science, also raises questions about their commitment to maintaining consistency of usage for basic terminology across different fields of science as should be expected for terms in ontology mapping with knowledge engineering for the semantic web. Therefore, in the present report, we clarify the origin of their FAIR principles by identifying our PDP and NPDS principles that constitute the historical precedent for their FAIR principles. Moreover, as the comprehensively summarizing phrase for all of our PDP and NPDS principles, we rename them the DREAM principles with the acronym DREAM for Discoverable Data with Reproducible Results for Equivalent Entities with Accessible Attributes and Manageable Metadata . Finally, we define numerically valid quantitative FAIR metrics to monitor and measure the DREAM principles from the perspective of the most important principle, ie, the Fair Acknowledgment of Information Records and Fair Attribution to Indexed Reports, for maintaining fair standards of citation in scholarly research and publishing.
A commentary and discussion of methodological bias in clinical research trials for dementia and Alzheimer's disease with particular attention to studies of amyloid imaging results disclosure. See also Safety of Disclosing Amyloid Imaging Results to MCI and AD Patients.
During program years 2014–2018, a total of 45 high school, college and graduate students have participated in the Brain Health Alliance Virtual Institute (BHAVI), a project-based learning and research training program. BHAVI students have published 14 conference papers in clinical and translational research informatics and data sciences. BHAVI uses frequent videoconferencing between students and mentors as its primary collaboration tool, and overcomes obstacles to on-line education with support from on-site advisors.
Basic question: Can a total-body PET scanner be exploited to improve evaluation, monitoring and measurement of both peripheral and central demyelination in multiple sclerosis (MS) patients? We assume here that demyelination outside the brain may involve at least the spinal cord if not also possibly some of the larger peripheral nerves outside the spinal column in a manner that might be detected with the greater sensitivity and resolution of the Explorer PET-CT scanner. Initial approach: Adopt a cost-effective and reduced-risk approach initially for a pilot study by using commercially available and already FDA-approved amyloid PET tracers to follow radiotracer uptake in white matter, thereby tracking demyelination versus remyelination for MS patients in comparison with normal healthy subjects. This initial approach with the Explorer total-body PET scanner used for amyloid imaging should hypothetically enable monitoring of increased versus decreased activity in both the peripheral nervous system (PNS) and the central nervous system (CNS), rather than only imaging the brain as performed in most conventional imaging evaluations for MS patients. Total-body amyloid PET imaging by the Explorer PET-CT scanner will be compared with analogous imaging by PET-MRI scanners. Future approach: Investigate other possible radiotracers (including those not yet FDA approved) that might be useful for monitoring demyelination, neuroinflammation and/or microglial activation in both the PNS and CNS of MS patients. Significance: Improved molecular imaging with new total-body PET scanners that provide greater sensitivity and better spatial resolution for quantitative measurement of demyelination and remyelination will support better decision-making for patient care and improved outcome measures for monitoring therapeutic drugs evaluated in clinical trials for the treatment of multiple sclerosis.
To maximize the contrast and clarity of coregistered PET (Positron Emission Tomography) and MR (Magnetic Resonance) brain scans, the application of certain color maps have been tested. Twelve of Matlab’s predefined color maps were applied on a PET-MR overlay to discover which are the easiest to read, yet are informative. The experiments are carried out in three phases, or constructs. These constructs have been executed on Matlab by functions within a custom-built library, which allows for the overlaying and resizing of brain scans. The first and second constructs consist of gray scale PET over colored MR and gray scale MR over colored PET, respectively. These first two constructs determine which type of scan, PET or MR, should be colored in an overlay, as well as the type of color map that should be applied. The third construct performs a fusion between colored PET and colored MR scans. A clinical trial consisting of twentyfive high school students has been completed. Students answered questions based on images generated by the Matlab software. Results confirm that the second construct, gray scale MR over colored PET, was preferred over the first. Next, students compared different color maps to identify which one allows for the best distinction between PET and MR scans in their overlay. Participants ranked the color maps, summer, copper, and cool, as first, second, and third, based on the contrast and clarity of the scans. Future studies will examine responses from brain imaging professionals and test custom-designed color maps on PET-MR overlays.
We describe CoVaSEA (Concept-Validating Search Engine Agent): an automated web crawler/query engine that is interoperable with the Nexus-PORTAL-DOORS System. The Nexus-PORTAL-DOORS System (NPDS) is a data management system that organizes repositories of lexical metadata (in PORTAL servers) and semantic representations (in DOORS servers) of resources. Due to the purpose built hybridized nature of NPDS, it is well placed to perform a variety of data analysis tasks. However, many of these tasks require records of semantic descriptions which are labor intensive to create and maintain due to the substantial and rapidly increasing quantities of brain related literature available on the open web. To remedy this, we created CoVaSEA with the intention of providing an automated method for users to navigate and expand the semantic records of brain literature in the NPDS directories. To this end, CoVaSEA integrates multiple features which benefit NPDS including: (A) An implementation of SPARQL query based search to allow retrieval and manipulation of RDF descriptions, (B) Targeted web-crawling for relevant articles from external biomedical literature databases to broaden NPDS records, and (C) Translation of free-form text into RDF triples to derive the semantic portrayals of lexical data. CoVaSEA consists of three principal components: the web-crawler, the lexical to semantic converter, and the SPARQL query engine. The web crawler retrieves articles along with their basic metadata (title, abstract, author(s), etc.) from several of biomedical literature databases via REST API. However, in order to capture a full semantic description of the data in each article, key RDF triples which describe the abstract are constructed. First, each of the unique nouns in the passage are registered via coreference resolution and pronomial anaphora. Then the sentences are parsed into constituency tree format so that the subject(s), verb(s), and object(s) can be extracted. Once the SVO triples are extracted, they are transformed into valid RDF by assigning unique resource identifiers (URI) to each part of the triples. This is accomplished by using various databases (i.e. MeSH) for terminology and select named entities, word sense disambiguation for standard words, and literals for any other sections. These triples are stored via the Scribe API in either a DOORS directory or a localized triplestore where they can be retrieved via the SPARQL query engine. In order to create a more conducive user experience, the query engine supports the capability to construct SPARQL queries from expressions in conjunctive normal form, thus circumventing the need to know SPARQL syntax. With the distinct advantage that the system is automated, CoVaSEA presents the capability to search “externally” to furnish large numbers of brain-related literature descriptions on a regular basis and search “internally” to provide a method of retrieving those descriptions, thus laying the groundwork for a variety of future NPDS applications for which semantic metadata stores of brain literature are a functional necessity.
Measuring the merits of a scholarly article only by how often other articles or social media posts cite it creates a perverse incentive for authors to avoid citing potential rivals. To uphold established standards of scholarship, institutions should also consider one or more metrics of how appropriately an article cites relevant prior work. This paper describes the general characteristics of the FAIR Attribution to Indexed Reports (FAIR) family of metrics, which we have designed for this purpose. We formulate five FAIR metrics suitable for use with primary research articles. Two measure adherence to best practices: number of correctly attributed background statements and number of genuinely original claims. Three measure specific deviations from best practices: number of misattributed background statements, number of background statements with missing references, and number of claims falsely indicated as original. We conclude with a discussion of plans to implement a web application for calculating metric values of scholarly works described by records in Nexus-PORTAL-DOORS System (NPDS) servers.
Measuring the merits of scholarly research articles only by citation counts and how often other research articles or social media messages cite a particular publication creates a perverse incentive for some authors to refrain from citing potential rivals. This dilemma has developed despite the historical publishing standard expected in peer review for citing and discussing related prior work. To encourage and support a countervailing incentive, research organizations should also consider metrics for how well and appropriately a scholarly article cites relevant prior work in the spirit of the classic phrase and metaphor standing on the shoulders of giants. We present a proposal for a family of such article-level metrics called the FAIR metrics and described as the FAIR Attribution to Indexed Reports or the FAIR Acknowledgment of Information Records.
Objective: To assess the psychological impact of disclosing a positive or negative amyloid brain scan result to symptomatic individuals with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD). Design: Prospective longitudinal cohort study. Setting: Florey Institute of Neuroscience & Mental Health, University of Melbourne, Australia. Participants: A total of 133 individuals aged 50–85 with MCI or mild AD enrolled in the study with data collected between October 2014 and June 2016. Interventions: Disclosure of amyloid imaging results to participants. Measurements: Positron emission tomography (PET) brain amyloid imaging with [18F]-NAV4694; psychometric scales including the Center for Epidemiologic Studies Depression (CES-D) scale, Geriatric Depression Scale (GDS), Hospital Anxiety and Depression Scales (HADS-A and HADS-D) and State-Trait Anxiety Inventory (STAI) performed before and after disclosure of amyloid imaging results. Results: We did not observe any worsening of psychological health with a panel of psychometric scales assessed on individuals to whom amyloid brain scan results were disclosed. Conclusions: We consider it safe, without apparent risk of harm to patients, to disclose amyloid imaging results to patients who have no prior history of neuropsychiatric illness.
To search and summarize research on biomedical questions, reasoning agents require access to high-quality semantic markup. The Nexus-PORTAL-DOORS v1.0 API and message exchange format empower organizations to manage and share their own collections of lexical metadata and RDF descriptions of knowledge resources. In this systems demonstration, NPDS servers built on Microsoft’s .NET framework distribute records to NPDS servers built on the MEAN solution stack for caching and distribution to clients.
The Nexus-PORTAL-DOORS System (NPDS) has been designed with the Hierarchically Distributed Mobile Metadata (HDMM) architectural style to provide an infrastructure system for managing both lexical and semantic metadata about both virtual and physical entities. We describe here how compatibility between version 0.9 of the NPDS schema, the new NPDS-interfacing ontologies, and the domain-specific concept-validating hypothesis-exploring ontologies allows NPDS to bootstrap the semantic web onto the more developed lexical web. We then describe how this system will serve as the foundation of a planned platform for automated meta-analysis.
Even though online databases make it easier than ever to access the biomedical and scientific literature about dementia, accelerating growth in the size of these databases has made it more difficult for humans to gather and analyze manually all articles relevant to any given topic. We document a Nexus-PORTAL-DOORS System (NPDS) Concept-Validating Search Engine Agent that can populate Nexus diristries with concept-validated metadata records for citations of journal articles found in literature databases.
Does the clinical status of patients with either Alzheimer’s disease or mild cognitive impairment when compared with the normal healthy status of control subjects have an effect on the co-registration accuracy of the participants PET and MRI brain scans? An initial evaluation reveals that a statistically significant difference may exist in co-registration accuracy with some popular algorithms for the different groups of participants’ brain scans. These differences suggest that investigators should use appropriate caution when reviewing fusion studies of co-registered PET and MRI brain scans.
The ability to view medical images as 3D objects, which can be explored interactively, has now become possible due to the advent of rapidly emerging virtual reality (VR) technologies. In the past, VR has been used as an educational tool for learning anatomy, a visualization tool for assisting surgery, and a therapeutic tool for rehabilitating patients with motor disorders. However, these older systems were either expensive to build or difficult to acquire and use. Exploiting the arrival of new consumer devices such as the Oculus Rift that are now affordable, we have developed a software application called BrainWatch for VR ready computers to enable 3D visualization and interactive exploration of DICOM data sets focusing on PET and MRI brain scans. BrainWatch software provides a unique set of 3 approaches for interacting with the virtual object which we have named the observatory scenario with an external camera, the planetarium scenario with an internal camera, and the voyager scenario with a mobile camera. A live interactive demonstration of BrainWatch VR with the Oculus Rift CV1 will be available for conference attendees to experience at EMBC 2017.
The Nexus-PORTAL-DOORS System (NPDS) has been designed with the Hierarchically Distributed Mobile Metadata (HDMM) architectural style to provide an infrastructure system for managing both lexical and semantic metadata about both virtual and physical entities. We describe version 0.8 of NPDS, including the separation of concerns between the original Problem-Oriented Registry of Tags And Labels (PORTAL) registries and the Domain Ontology Oriented Resource System (DOORS) directories, the combined registry and directory functionality of Nexus diristries, and the RESTful read-only web service API through which resource representation metadata records can be retrieved from these NPDS servers. We also introduce Scribe registrars with a corresponding RESTful read-write web service API for management of metadata records by both software agents accessing the web services directly and human users accessing them indirectly via web applications.
In routine clinical imaging, PET and MR images often undergo co-registration, however, methods for co-registration may vary. The significance of differences between methods has not been previously determined. Registration accuracy was calculated both qualitatively and quantitatively using different metrics. Both the quantitative metrics and subsequent visual inspection confirm that there exists a significant difference between different registration methods. Because a difference does exist across co-registration methods, clinicians and researchers must take appropriate care when choosing what method to use for PET-MR co-registration.
In a world of rapidly emerging commercial Virtual Reality (VR) technologies, such as Oculus Rift, the ability to view medical images as an interactive 3D object, which can be virtually entered, becomes a possibility. In the past, virtual reality has been used as an educational tool, for therapy in motor disorders, and also as a visualization tool for surgery, however, many of these systems have been a combination of incredibly costly and often difficult to come by. Using these improved and more readily available technologies, we have created an application for use with a VR enabled computer and Oculus Rift to allow the 3D visualization of DICOM datasets, specifically MRI and PET brain scans.
The PORTAL-DOORS system (PDS) has been designed as a resource metadata management system intended to support applications such as automated searches of online resources and meta-analyses of published literature. PDS comprises a network of Problem Oriented Registry of Tags and Labels (PORTAL) lexical registries and Domain Ontology Oriented Resource System (DOORS) semantic directories. Here we introduce a PDS-compliant concept-validating registry and hypothesisexploring ontology that organizes focal-onset dementias including Sensory-Onset, Language-Onset and Motor-ONset (SOLOMON) dementias with novel classifying and relating concepts. This approach facilitates semantic search of resources and exploration of hypotheses related to neurodegeneration. SOLOMON interoperates with other PDS registries and ontologies including BrainWatch, ManRay and GeneScene.
The PORTAL-DOORS system (PDS) has been designed as a resource metadata management system intended to support applications such as automated searches of online resources and meta-analyses of published literature. We present a methodological approach with a PDS-compliant concept-validating registry and hypothesis-exploring ontology that organizes focal-onset dementias including Sensory-Onset, Language-Onset and Motor-ONset(SOLOMON) dementias with novel classifying and relating concepts. This approach facilitates semantic search of resources and exploration of hypotheses related to neurodegeneration. SOLOMON interoperates with other PDS registries and ontologies including BrainWatch, ManRayand GeneScene.
Clinical telegaming integrates telecare and videogaming to enable a more convenient and enjoyable experience for patients when providers diagnose, monitor, and treat a variety of health problems via web-enabled telecommunications. In recent years, clinical telegaming systems have been applied to physical therapy and rehabilitation, evaluation of mental health, and prevention and management of obesity and diabetes. Parkinson’s disease (PD) is suitable for development of new clinical telegaming applications because PD patients are known to experience motor symptoms that can be improved by physical therapy. Recent research suggests that sensory processing deficits may also play an important role in these motor impairments because successful motor function requires multisensory integration. In this paper, we describe a new web-enabled software system that uses clinical telegaming to evaluate and improve multisensory integration ability in users. This software has the potential to be used in diagnostic and therapeutic telegaming for PD patients.
Alzheimer disease is the cause of up to one-third of cases of primary progressive aphasia or corticobasal syndrome. The primary objective of this study was to determine the accuracy of 18F-FDG PET metabolic imaging for the detection of Alzheimer disease in patients with primary progressive aphasia or corticobasal syndrome. Methods: A cohort of patients (n = 94), including those with an expert clinical diagnosis of logopenic (n = 19), nonfluent (n = 16), or semantic (n = 13) variants of primary progressive aphasia, corticobasal syndrome (n = 14), or Alzheimer disease (n = 24), underwent 18F-FDG metabolic and 11C-labeled Pittsburgh compound B (11C-PiB) amyloid PET brain imaging. 18F-FDG PET scans interpreted with Neurostat and 3D-SSP displays were classified as revealing Alzheimer disease or “other” by interpreters who were unaware of the clinical assessments and 11C-PiB PET results. 11C-PiB PET imaging was considered to be the diagnostic reference standard, with a threshold standardized uptake value ratio of 1.5 being indicative of Alzheimer disease pathology. To address possible bias from subgroup selection for the Alzheimer disease binary classifier, we calculated both conventional and balanced accuracies. Results: Diagnoses of Alzheimer disease based on 18F-FDG PET resulted in 84% accuracy (both conventional and balanced). In comparison, diagnoses based on clinical assessments resulted in 65% conventional accuracy and 67% balanced accuracy. Conclusion: Brain 18F-FDG PET scans interpreted with Neurostat and 3D-SSP displays accurately detected Alzheimer disease in patients with primary progressive aphasia or corticobasal syndrome as focal-onset dementias. In such diagnostically challenging cohorts, 18F-FDG PET imaging can provide more accurate diagnoses, enabling more appropriate therapy.