Search

The objective of the study was to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the Howard County, Maryland, general population and demographic subpopulations attributable to natural infection or coronavirus disease 2019 (COVID-19) vaccination and to identify self-reported social behaviors that may affect the likelihood of recent or past SARS-CoV-2 infection. A cross-sectional, saliva-based serological study of 2,880 residents of Howard County, Maryland, was carried out from July through September 2021. Natural SARS-CoV-2 infection prevalence was estimated by inferring infections among individuals according to anti-nucleocapsid immunoglobin G levels and calculating averages weighted by sample proportions of various demographics. Antibody levels between BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) recipients were compared. Antibody decay rate was calculated by fitting exponential decay curves to cross-sectional indirect immunoassay data. Regression analysis was carried out to identify demographic factors, social behaviors, and attitudes that may be linked to an increased likelihood of natural infection. The estimated overall prevalence of natural infection in Howard County, Maryland, was 11.9% (95% confidence interval, 9.2% to 15.1%), compared with 7% reported COVID-19 cases. Antibody prevalence indicating natural infection was highest among Hispanic and non-Hispanic Black participants and lowest among non-Hispanic White and non-Hispanic Asian participants. Participants from census tracts with lower average household income also had higher natural infection rates. After accounting for multiple comparisons and correlations between participants, none of the behavior or attitude factors had significant effects on natural infection. At the same time, recipients of the mRNA-1273 vaccine had higher antibody levels than those of BNT162b2 vaccine recipients. Older study participants had overall lower antibody levels compared with younger study participants. The true prevalence of SARS-CoV-2 infection is higher than the number of reported COVID-19 cases in Howard County, Maryland. A disproportionate impact of infection-induced SARS-CoV-2 positivity was observed across different ethnic/racial subpopulations and incomes, and differences in antibody levels across different demographics were identified. Taken together, this information may inform public health policy to protect vulnerable populations. IMPORTANCE We employed a highly innovative noninvasive multiplex oral fluid SARS-CoV-2 IgG assay to ascertain our seroprevalence estimates. This laboratory-developed test has been applied in NCI's SeroNet consortium, possesses high sensitivity and specificity according to FDA Emergency Use Authorization guidelines, correlates strongly with SARS-CoV-2 neutralizing antibody responses, and is Clinical Laboratory Improvement Amendments-approved by the Johns Hopkins Hospital Department of Pathology. It represents a broadly scalable public health tool to improve understanding of recent and past SARS-CoV-2 exposure and infection without drawing any blood. To our knowledge, this is the first application of a high-performance salivary SARS-CoV-2 IgG assay to estimate population-level seroprevalence, including identifying COVID-19 disparities. We also are the first to report differences in SARS-CoV-2 IgG responses by COVID-19 vaccine manufacturers (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]). Our findings demonstrate remarkable consistency with those of blood-based SARS-CoV-2 IgG assays in terms of differences in the magnitude of SARS-CoV-2 IgG responses between COVID-19 vaccines., Competing Interests: The authors declare no conflict of interest.

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises., (© 2023. The Author(s).)

Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison. We demonstrate common pitfalls in a designing the human performance evaluation and propose a framework for the evaluation of human performance, illustrating guiding principles for a successful comparison. These principles are first, to design the human evaluation with an understanding of the differences between human and algorithm cognition; second, to match trials between human participants and the algorithm evaluation, and third, to employ best practices for psychology research studies, such as the collection and analysis of supplementary and subjective data and adhering to ethical review protocols. We demonstrate our framework's utility for designing a study to evaluate human performance on a one-shot learning task. Adoption of this common framework may provide a standard approach to evaluate algorithm performance and aid in the reproducibility of comparisons between human and machine learning algorithm performance., (© 2022. The Author(s).)

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Rhodes, Cowley, Huang, Gray-Roncal, Wester and Drenkow.)

Background: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods., Results: We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines., Conclusions: Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains., (© The Author(s) 2020. Published by Oxford University Press GigaScience.)

Background: As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data formats, and specific user needs in an easily accessible package., Results: We present substrate, a data-visualization framework designed to simplify communication and code reuse across diverse research teams. Our platform provides a simple, powerful, browser-based interface for scientists to rapidly build effective three-dimensional scenes and visualizations. We aim to reduce the limitations of existing systems, which commonly prescribe a limited set of high-level components, that are rarely optimized for arbitrarily large data visualization or for custom data types., Conclusions: To further engage the broader scientific community and enable seamless integration with existing scientific workflows, we also present pytri, a Python library that bridges the use of substrate with the ubiquitous scientific computing platform, Jupyter. Our intention is to lower the activation energy required to transition between exploratory data analysis, data visualization, and publication-quality interactive scenes., Competing Interests: Competing interests The authors declare that they have no competing interests.